Research Publications

Authors

Nurjannah; Satra, Ramdan, Universitas Muslim Indonesia;

Abstract

Program Kemitraan Masyarakat (PKM) ini bertujua untuk meningkatkan kemampuan baca tulis Al-Qur’an pengurus dan anggota Majelis Taklim An Nur Desa Borikamase melalui pemanfaatan teknologi berbasis Android. Latar belakang kegiatan ini adalah keterbatasan metode pembelajaran yang efektif, keterbatasan tenaga pengajar, serta rendahnya pemanfaatan teknologi sebagai media pembelajaran mandiri. Meskipun aebagian besar anggota majelis taklim telah memiliki perangkat Android, mereka belum terbiasa menggunakan aplikasi berbasis Android untuk belajar baca tulis al Qur’an. Kondisi ini menyebabkan proses belajar mengaji cenderung bergantung pada pertemuan tatap muka yang bersifat terbatas dan tidak berkelanjutan. Pelaksanaan program meliputi beberapa tahapan, yaitu persiapan (identifikasi peserta dan pemilihan aplikasi), pelatihan penggunaan aplikasi Al-Qur’an digital, praktik baca

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Authors

Widiyanti, Nabila; Hasanuddin, Tasrif; Azis, Huzain, Universitas Muslim Indonesia;

Abstract

Managing multiple academic tasks with overlapping deadlines remains challenging for university students, while conventional task-management applications still require substantial manual organization and prioritization. This study develops an LLM-based AI Task Agent that enables conversational academic task management through Telegram and workflow automation. Method The proposed system integrates Telegram as the interaction interface, n8n for workflow orchestration, an LLM-based AI agent for natural-language interpretation and tool selection, Google Sheets for task-data operations, PostgreSQL for conversational memory, and scheduled workflows for automated reminders. The system supports Create, Read, Update, and Delete operations, contextual priority recommendations based on deadline, urgency, and lecturer strictness, and proactive reminders. Functional performance and response time were evaluated across the primary system functions. Results and Discussion Create, Read, Update, and Delete operations achieved 100% functional accuracy, while priority recommendation and automated reminder functions achieved 95%. Recorded processing times ranged from 3.1 to 3.5 seconds, with an average of approximately 3.32 seconds. The results demonstrate that separating LLM-based interpretation from predefined external tool execution enables reliable conversational task management while maintaining controlled data operations. Conclusion The proposed LLM-based AI Task Agent demonstrates the feasibility of integrating conversational interaction, executable task-management functions, contextua

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Authors

Atmajaya, Dedy; Basalamah, Abdullah; Kurniati, Nia; Iqbal, Muhammad; Jasmin, Thalita Sherly Putri, Universitas Muslim Indonesia;

Abstract

Temperature monitoring is critical in hydroponic cultivation because it influences nutrient solubility, dissolved oxygen, and root uptake, yet low-cost digital sensors commonly used in Internet of Things (IoT) systems may experience accuracy degradation during long-term deployment. This study develops a low-cost IoT monitoring platform for nutrient film technique (NFT) hydroponics and characterizes temperature-sensor drift under continuous operating conditions. Method The system employed two redundant temperature sensors in the nutrient channel and one ambient sensor, with measurements timestamped, filtered, stored locally at the edge, and visualized through a cloud dashboard. Temperature data were recorded hourly for 40 days, producing 960 observations per sensor. Drift was evaluated from the deviation between the primary and redundant channel sensors using mean absolute error, maximum absolute deviation, standard deviation, drift onset, and estimated drift rate. Results and Discussion The primary sensor showed a mean absolute deviation of 0.82 °C over the full monitoring period and a maximum deviation of 1.47 °C. Sensor agreement remained close during the first 10 days but diverged after approximately day 12, with late-period MAE increasing to 1.18 °C and an estimated drift rate of about 0.04 °C/day. Conclusion Long-term drift in low-cost temperature sensors can materially affect hydroponic monitoring accuracy, and redundant sensing provides a practical baseline for future adaptive edge-based calibration methods.

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Authors

Idris, Fairus P; Sharief, Suchi A ; Atmajaya, Dedy; Asrina, Andi, Universitas Muslim Indonesia;

Abstract

Exclusive breastfeeding is scientifically known to reduce child mortality; however, coverage remains low in Bulukumba Regency, which has the lowest rates in South Sulawesi Province, Indonesia. This study aimed to examine the influence of the Theory of Planned Behavior (TPB) on exclusive breastfeeding behavior among mothers in the region. A quantitative cross-sectional study was conducted with 161 mothers of infants aged 6–12 months. Data were collected using a validated and reliable questionnaire and analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The findings indicate that maternal attitudes towards exclusive breastfeeding (β= 0.619) and perceived control (β= 0.338) had significantly positive effects on breastfeeding intention, whereas subjective norms were not significantly associated with breastfeeding intention (p= 1.000). Furthermore, attitudes, subjective norms

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Authors

Hasnawi, Mardiyyah; Astuti, Wistiani; Puspitasari, Andi; Kurniati, Nia; Indra, Dolly

Abstract

Data processing in waste bank management systems faces substantial challenges when extracting structured information from unstructured textual data containing transaction records, customer information, and waste categorization details. This study develops and validates an automated Named Entity Recognition (NER) system based on the IndoBERT transformer architecture to process textual records from Makassar waste bank operations. The approach fine-tunes the indolem/indobert-base-uncased model through domain-specific adaptation targeting waste management vocabulary and operational terminology. The dataset comprises 975 textual records collected from 8 Waste Bank Units in Makassar City. These records were systematically annotated using the BIO tagging scheme for eight entity types: B-LOCATION, B-PERSON, B-WASTE_CATEGORY, B-WASTE_TYPE, I-PERSON, I-WASTE_CATEGORY, I-WASTE_TYPE, and O. The dataset was partitioned into training (682 samples, 69.9%), validation (146 samples, 15.0%), and test sets (147 samples, 15.1%) using stratified sampling methodology with high inter-annotator agreement (κ=0.91). Experimental results show outstanding performance with 94.37% test accuracy, 90.65% precision, 94.37% recall, and 92.21% F1-score, outperforming general-purpose Indonesian NER approaches. Perfect performance was achieved for waste type recognition (B-WASTE_TYPE: 100% F1-score) and location identification (B-LOCATION: 100% F1-score), while waste category classification reached 96% F1-score. This implementation successfully automates entity extraction from Makassar waste bank textual data, reducing manual processing time by 95% while maintaining high accuracy levels. This research makes important contributions to Indonesian environmental natural language processing. These contributions include transformer adaptation methodologies for resource-constrained domains, validated IndoBERT performance on Indonesian waste bank data, the first Indonesian waste management NER dataset, and demonstrated feasibility of NLP-based environmental policy systems.

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Authors

Ashar, Muh As' ad; Salim, Yulita; Alwi, Erick Irawadi

Abstract

The Free Nutritious Meal Program (MBG) is a government initiative aimed at improving public nutritional quality and reducing stunting rates in Indonesia. The implementation of this program has generated various public responses, particularly those expressed through social media platform X. Therefore, sentiment analysis is required to identify public opinion tendencies toward the program. This study aims to analyze public sentiment toward the Free Nutritious Meal Program using the Bidirectional Encoder Representations from Transformers (BERT) method, specifically the IndoBERT model. The dataset consists of 10,925 tweets collected through scraping using the X API and a public dataset from Kaggle. The data were processed through preprocessing stages including cleaning, case folding, and normalization. Sentiment labeling was conducted using a lexicon-based approach combined with manual labeling into three sentiment classes, positive, negative, and neutral. To address data imbalance, random oversampling was applied prior to model training. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the IndoBERT model performs well in classifying public sentiment, achieving an accuracy of 90.2%, precision of 90.4%, recall of 90.2%, and an F1-score of 90.3%. Overall, public sentiment toward the Free Nutritious Meal Program is predominantly positive.

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Authors

Mz, Muhammad Fahd; Salim, Yulita; Irawati

Abstract

Layanan konseling mahasiswa merupakan salah satu layanan non-akademik yang berperan penting dalam mendukung keberhasilan studi dan kesejahteraan mahasiswa. Namun, keterbatasan akses informasi serta belum optimalnya pemanfaatan teknologi dapat menjadi kendala dalam penyelenggaraan layanan konseling. Penelitian ini bertujuan untuk merancang user interface layanan konseling mahasiswa Fakultas Ilmu Komputer Universitas Muslim Indonesia yang berorientasi pada kebutuhan pengguna. Metode yang digunakan dalam penelitian ini adalah Human-Centered Design (HCD), yang meliputi tahapan empathize, define, ideate, prototype, dan evaluate. Pendekatan ini digunakan untuk memahami kebutuhan dan permasalahan pengguna sebagai dasar dalam perancangan antarmuka sistem. Hasil penelitian berupa rancangan user interface layanan konseling mahasiswa yang memiliki alur layanan terstruktur, fungsi yang jelas, serta antarmuka yang mudah dipahami oleh pengguna. Pemodelan sistem dilakukan menggunakan use case diagram, activity diagram, dan sequence diagram untuk menggambarkan interaksi dan alur proses layanan konseling. Rancangan user interface yang dihasilkan diharapkan dapat meningkatkan kemudahan akses, kenyamanan penggunaan, dan efektivitas layanan konseling bagi mahasiswa Fakultas Ilmu Komputer Universitas Muslim Indonesia

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Authors

Ashad, Bayu Adrian; Ramdaniah; Mansur; Ilyas, Andi Muhammad; Siswanto, Agus; Jaya, Arif

Abstract

Monitoring kualitas air merupakan salah satu faktor penting dalam keberhasilan budidaya tambak karena berpengaruh terhadap pertumbuhan, kesehatan, dan produktivitas ikan. Namun, sebagian besar masyarakat pembudidaya masih melakukan pemantauan kualitas air secara manual sehingga perubahan kondisi air sering terlambat diketahui. Kegiatan Pengabdian kepada Masyarakat ini bertujuan memberdayakan masyarakat melalui penerapan sistem monitoring kualitas air tambak berbasis Internet of Things (IoT). Kegiatan dilaksanakan di Kabupaten Pangkajene dan Kepulauan (Pangkep), Sulawesi Selatan, menggunakan pendekatan partisipatif yang meliputi penyuluhan, implementasi sistem monitoring berbasis IoT, pelatihan, pendampingan, dan evaluasi. Sistem yang diterapkan memanfaatkan sensor suhu, pH, salinitas, dan dissolved oxygen (DO) yang terhubung dengan mikrokontroler ESP32 untuk mengirimkan data secara real-time ke dashboard berbasis web. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan peserta dalam memanfaatkan teknologi IoT, yang ditunjukkan oleh meningkatnya kemampuan peserta dalam memahami konsep monitoring kualitas air, mengenali fungsi sensor, mengoperasikan sistem monitoring, serta membaca data pada dashboard. Penerapan teknologi IoT memberikan kemudahan bagi masyarakat dalam memantau kondisi kualitas air secara lebih cepat dan efektif sehingga mendukung pengelolaan budidaya tambak yang lebih produktif dan berkelanjutan.

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Authors

Masnur, Andi Putri Atirah; Hayati, Lilis Nur; Faradibah, Amaliah, Universitas Musllim Indonesia

Abstract

Art studios play an important role in preserving traditional dance as a valuable cultural heritage; however, many still face operational challenges such as limited promotion, manual administrative processes, and unstructured rental services that reduce management effectiveness and public access. This study aims to design a web-based Management Information System (MIS) for Sanggar Lontara in Bone Regency using the prototyping method to support administrative management and service rental activities in accordance with user needs. The system was developed through an iterative prototyping approach involving requirement analysis, design, implementation, evaluation, and refinement with active participation from studio administrators. System evaluation consisted of alpha testing using black-box testing and beta testing through a Likert-scale questionnaire distributed to 32 respondents across six assessment indicators. The developed system includes service catalogs, online booking, payment proof uploads, administrative verification, reporting, portfolio galleries, studio information, and role-based access for administrators, renters, and managers. Alpha testing demonstrated a 100% success rate for core functionalities, while beta testing achieved a user satisfaction index of 88.02%, classified as “Very Good,” indicating that the system is feasible for implementation with minor usability improvements.

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Authors

Astuti, Wistiani; Alwi, Erick Irawadi; Fattah, Farniwati; Hasanuddin, Tasrif; Sari, Ulfa; Almagfirah, Ainur Rahma, Universitas Musllim Indonesia

Abstract

Introduction: Tea leaf diseases can substantially reduce crop quality and productivity, making early and accurate diagnosis important for effective disease management. This study compares ResNet50 and ResNet101 as pretrained deep feature extractors combined with Support Vector Machine (SVM) to determine whether a deeper residual architecture can improve discrimination among visually similar tea leaf disease classes. Method: Images were obtained from the Kaggle “Identifying Disease in Tea Leaves” dataset comprising eight classes. Data augmentation increased each class to 800 images, yielding 6,400 images that were divided into training and testing sets using an 80:20 ratio. ResNet50 and ResNet101 pretrained on ImageNet were used as fixed feature extractors, and the resulting feature vectors were standardized and classified using an RBF-kernel SVM. Results and Discussion: ResNet101–SVM achieved the best performance with 97.97% accuracy and precision, recall, and F1-score of 98%, substantially outperforming ResNet50–SVM, which achieved 89.15% accuracy, 90% precision, 89% recall, and 89% F1-score. The deeper ResNet101 architecture provided more discriminative representations for visually similar disease patterns, although a small number of misclassifications remained. Conclusion: ResNet101 combined with SVM provides a more accurate and reliable framework than ResNet50–SVM for multi-class tea leaf disease classification and offers a promising foundation for automated disease diagnosis systems.

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Authors

Tenripada, Andi Ulfah; Syafie, Lukman; Mu'min, Muh; Ataillah, Raqhib; Nawir, Muh, Universitas Musllim Indonesia

Abstract

Introduction: Soybean productivity in Indonesia remains below national demand, while the effectiveness of Rhizobium inoculation depends strongly on dynamic soil conditions such as pH, moisture, temperature, and nitrogen availability. This study develops an Internet of Things (IoT)-based decision support system for adaptive Rhizobium inoculation in highland soybean cultivation. Method: The system integrates a Soil NPK RS485 Modbus sensor, ESP32-WROOM-32D microcontroller, MQTT communication, and the SMARTO web dashboard to monitor four environmental parameters in real time. A Mamdani Fuzzy Inference System was implemented using 17 membership functions and 25 expert-derived IF–THEN rules, with centroid defuzzification producing Rhizobium dose recommendations from 0 to 200 g/ha. Results and Discussion: Field readings of pH 7.5, soil moisture 55%, temperature 26°C, and nitrogen 155 mg/kg generated a recommendation of 16.56 g/ha, classified as Very Low, with a pump volume of 33 mL/ha. Expert validation across 30 simulated highland scenarios produced an overall agreement rate of 83.3%, demonstrating satisfactory consistency between system recommendations and agronomic judgment. Conclusion: The proposed IoT-Fuzzy DSS demonstrates the feasibility of location-specific, real-time, and interpretable Rhizobium inoculation support for highland soybean cultivation, providing a practical foundation for precision biological-input management.

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Authors

Wulandari, LMC; Nurhayati, Lilis; Dimas, Ravael; Pijoh, Fidelchristo, Prodi Teknik Industri, Fakultas Teknik, Universitas Katolik Darma Cendika, Indonesia, 61557

Abstract

Sector Logistics plays an important role in maintaining the smoothness and efficiency of national and global supply chains, especially for third-party logistics ( 3PL ) service providers who face demands for reliability and speed of service amidst global competition. The complexity of the logistics process creates the need for a structured and measurable performance evaluation model. This study aims to apply the Supply Chain Operations Reference ( SCOR ) model in identifying and prioritizing key performance indicators ( KPI ) in a 3PL company . The method used involves distributing questionnaires to decision makers in operational and managerial fields. The weights of SCOR performance attributes including reliability, responsiveness, flexibility, cost measures, and asset management efficiency are calculated using the Fuzzy Analytic Hierarchy Process ( FAHP ) method. Furthermore, the Technique for Order Preference by Similarity to Ideal Solution ( TOPSIS ) is used to determine KPI priorities at PT.X. The results of the study show that there are 17 indicators to measure the performance of the freight forwarding sector with the largest weight being operator reliability (0.251) on the reliability attribute, the number of on-time deliveries (0.694) on the responsiveness attribute, load flexibility (0.317) on the flexibility attribute, shipping costs per km (0.379) on the cost attribute and cash-to-cash cycle time (0.479) on the asset management attribute. The SCOR model has proven effective as an initial framework in measuring the performance of 3PL logistics service providers, because it is able to integrate various aspects of performance systematically and quantitatively.

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Authors

Nurhayati, Lilis; Andrian,David, Universitas Musllim Indonesia

Abstract

The utilization of log residue waste as an alternative raw material in the wood processing industry has the potential to improve resource efficiency; however, material heterogeneity often leads to product quality issues. This study aims to identify the dominant types of defects and their contributing factors in wooden cable pallets made from log residue waste, as well as to determine quality improvement priorities through the integration of Seven Tools of Quality and Quality Function Deployment (QFD). The study was conducted at UD Nurari Perdana Kabelindo, Sidoarjo, using production and defect data collected during September 2025 through observations, interviews, documentation, and questionnaires. The Seven Tools analysis employed check sheets, Pareto charts, and cause-and-effect diagrams to identify dominant defects and their root causes. The results were subsequently integrated into QFD through the development of a House of Quality to translate customer needs into priority technical characteristics. Customer requirements were determined based on product quality attributes obtained from 30 customer respondents using a census method. The results showed a defect rate of 11.51% of total production, with the dominant defects consisting of thickness deviation, wavy surfaces, knot defects, and asymmetrical circular shapes. The QFD analysis revealed that operator precision and raw material selection were the top priorities for quality improvement. These findings demonstrate that the integration of Seven Tools and QFD is effective in linking process defect analysis with customer needs to support sustainable product quality control.

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Authors

Mubarak, Mush’ab Al; Syahar, A. Ulfah Tenripada; Kasim, Fadly, Program StudiTeknik Informatika, FakultasIlmu Komputer, UniversitasMuslim Indonesia, Jl.Urip Sumoharjo Km.05, Kota Makassar, Sulawesi Selatan, Indonesia; Hayati, Lilis Nur; Mansyur, St Hajrah, Program StudiSistem Informasi, FakultasIlmu Komputer, UniversitasMuslim Indonesia, Jl.Urip Sumoharjo Km.05, Kota Makassar, Sulawesi Selatan, Indonesia

Abstract

Vanilla is a high-value plantation commodity whose productivity is significantly affected by plant diseases that are difficult to identify accurately using conventional methods. This study aims to develop a mobile-based vanilla plant disease identification system using a Convolutional Neural Network (CNN) with the ResNet152 architecture. The dataset consists of primary field-acquired images, which were augmented to produce a total of 1,616 images across five disease classes. The model was trained using a transfer learning scheme with parameter adjustments designed to handle variations in field lighting conditions, image angles, and real-world visual characteristics. Experimental results demonstrate that the proposed ResNet152 model achieves high and stable classification accuracy. The integration of the trained model into a mobile application enables fast and practical disease diagnosis in real plantation environments. The novelty of this study lies in the field-oriented optimization of the ResNet152 model and its direct deployment in a mobile diagnostic system tailored for vanilla plant disease identification.

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Authors

Arif, Husnul Khotimah; Hayati, Lilis Nur; Sugiarti, Universitas Musllim Indonesia

Abstract

Lack of early treatment leads many patients to seek medical attention only when they have Grade IV Internal Hemorrhoids. Without proper treatment, this condition can lead to serious complications such as severe pain and anemia due to repeated bleeding. Hemorrhoids are a medical condition characterized by swelling of the veins in the anus and lower rectum, which can cause pain, itching, and bleeding during bowel movements. Hemorrhoids include 18 symptoms and are divided into six types: grades I-IV internal hemorrhoids, external hemorrhoids, and thrombosed hemorrhoids. The purpose of this research is to develop an expert system application for diagnosing hemorrhoids to help the public determine the severity of their condition. This system is built using a knowledge base obtained from doctors who have treated hemorrhoids. The Certainty Factor method helps explain the level of confidence in a symptom based on expert knowledge, so users can obtain initial information before seeking further examination by a medical professional. From the trial of 22 samples with a comparison of the system test results with experts, there were 4 that did not match, so the system obtained a percentage of 81,82% system accuracy in diagnosing hemorrhoids, so this application makes it easier for users to diagnose the type of hemorrhoids.

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Authors

Salsabilah, Nadya; Irawati; Hayati, Lilis Nur, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Tomatoes are one of the most widely cultivated and consumed crops, but they are highly susceptible to disease attacks. The main diseases that often attack tomato plants are early blight and late blight. This study compares two machine learning-based classification methods, namely Convolutional Neural Network (CNN) and Naïve Bayes, in detecting tomato leaf diseases. The dataset used consists of 1,255 images obtained from Kaggle, which have been processed and divided into three data ratio scenarios (70:30, 80:20, and 90:10) for training and testing. The results showed that CNN is superior to Naïve Bayes, with the highest accuracy reaching 83.01%, while Naïve Bayes only achieved 34%. With better stability and accuracy, CNN has the potential to help farmers detect diseases more quickly and increase agricultural productivity

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