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All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal INSYPRO (Information System and Processing) JURNAL INSTEK (Informatika Sains dan Teknologi) JURTEKSI JTP - Jurnal Teknologi Pendidikan Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Ainet : Jurnal Informatika Jurnal ABDINUS : Jurnal Pengabdian Nusantara Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia Buletin Sistem Informasi dan Teknologi Islam Jurnal Informatika Progres Jurnal INFOTEL Pangulu Abdi: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmu Pengetahuan dan Teknologi Bagi Masyarakat
LUKMAN ANAS
UNIVERSITAS MUHAMMADIYAH MAKASSAR
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Prediksi Tingkat Kelulusan Menggunakan K-Means Pada Program Studi Informatika Unismuh Makassar Irhamna Rachman, Fahrim; Mujadilah, Siti; Wahyuni, Titin; Anas, Lukman
JURNAL FASILKOM Vol. 13 No. 3 (2023): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v13i3.6061

Abstract

Predicting timely graduation brings numerous benefits not only to students but also to the university itself. Creating a graduation prediction model assists students and academic advisors in fostering a positive environment that encourages on-time graduation by developing a predictive model for graduation rates using the K-means data mining method in the Informatics study program at Universitas Muhammadiyah Makassar. This method is used to cluster students based on attributes such as total credits taken, semester Grade Point Average (GPA), and overall Cumulative Grade Point Average (CGPA). The clustering aims to identify patterns and characteristics of student graduation. Data from several semesters is collected and preprocessed, including data normalization and transformation. The research steps involve data preprocessing, cluster labeling, distance calculation to cluster centers, and result analysis. The analysis shows that the K-means method can generate student clusters with varying graduation rate patterns. The formed clusters can be interpreted as groups of students with potential for timely graduation or groups needing more attention to achieve on-time graduation. Empirical validation is performed by comparing K-means prediction results with actual graduation data. Accuracy measurement involves calculating the percentage of similarity between predictions and actual data. Empirical validation results demonstrate the accuracy level, which can serve as a benchmark for assessing the performance of this prediction model. This study aims to provide deeper insights into factors influencing student graduation and potentially support decision-making at the academic level. Keywords: Graduation Prediction, Data Mining, K-Means, Analysis, Clustering, Empirical Validation.
Penguatan Kelembagaan Dan Pemasaran Produksi Bumdes Mandiri Desa Pitusunggu Kec. Ma’rang Kab. Pangkep Saleh, Syafiuddin; Muhsin, Arief; Anas, Lukman; Putra, Dian Pramana; Basir, Basri
Jurnal IPMAS Vol. 2 No. 1 (2022): April 2022
Publisher : Pustaka Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54065/ipmas.2.1.2022.106

Abstract

Tujuan yang menjadi sasaran dari program pengabdian ini kepada mitra yang meliputi; (1) Pengambangan jejaring pemasaran secara online hasil diversifikasi olahan Rumput Laut dan Ikan Bandeng dengan media sosial; (2) Peningkatan varian diversifikasi dan modernisasi pengemasan produk olahan rumput laut dan ikan bandeng; (3) Pembuatan poster produk dan video profil olahan rumput laut dan ikan bandeng; serta (4) Penjualan dan promosi hasil olahan rumput laut dan ikan bandeng dengan sistem E-Commerce. Hasil kegiatan program pengabdian masyarakat ini menunjukkan adanya keterampilan yang signifikan terhadap mitra. Yang pertama, mitra mengalami kemajuan pengetahuan dalam menggunakan fungsi media sosial secara luas dimana sebelumnya hanya untuk komunikasi. Mereka telah mampu menggunakan IG, whatsapp, twitter, linkedin, dan youtube untuk promosi hasil olahan rumput laut. Yang kedua, melalui program ini anggota bumdes diberikan edukasi berupa pengembangan diversifikasi olahan rumput laut berupa Brownies disertai pengemasan yang modern dan menarik. Yang ketiga, proses produksi olahan rumput laut dan ikan bandeng direkam dan dibuat video yang menarik termasuk pembuatan poster dan label produksi. Yang keempat, dikembangkan aplikasi e-comdes Pitusunggu untuk lapak online bumdes. Melalui e-comdes ini, mitra dapat dapat menjual hasil produksinya secara online termasuk promosi ke masyarakat secara umum. E-ComDes Pitusunggu melalui laman https://ecomdes.id dapat diakses dimana saja dan masyarakat umum dapat melakukan registrasi untuk ikut serta menjajakan dagangannya pada sistem tersebut.
Sistem Pendukung Kepuntusan Penentuan Varietas Bawang Merah Menggunakan Metode Simple Additive Weighting (SAW) di Desa Bonto Lojong Kab. Bantaeng Alfiani, Ananda; Lukman Anas; Lukman
Ainet : Jurnal Informatika Vol. 7 No. 1 (2025): Maret (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/tk2pde16

Abstract

 ANANDA ALFIANI. Decision Support System for Determining Shallot Varieties Using the Saw Method (Simple Additive Weighting) in Bonto Lojong Village, Kab. Bantaeng (Supervised by Lukman Anas, S.Kom., MT., and Lukman SKM, S.Kom., MT.,).The research carried out aimed to determine the types of shallot varieties in Bantaeng Regency, especially in Bonto Lojong Village, which was web-based. This system helps shallot farmers in selecting suitable varieties to be used as seeds using the Simple Additive Weighting (SAW) method. The research design used is Unified Modeling Language (UML) which is designed in a structured manner consisting of use case diagram model designs, activity diagrams, sequence diagrams and class diagrams. The text editor used in building this system is Sublime Text, while the programming language uses PHP, JavaScript and MySQL for database processing. In this research, data collection was obtained through observation, interviews and documentation. The method used in the research is the Simple Additive Weighting (SAW) method. Results from the application of the Decision Support System for Determining Shallot Varieties Using the Saw Method (Simple Additive Weighting) in Bonto Lojong Village, Kab. Bantaeng helps and makes it easier for farmers to determine the variety of shallots in their land by giving the highest value to the types of shallot varieties to be used as seeds for the next planting period.  Keywords: Decision support system, spk of shallot varieties, SAW method.
PENERAPAN ALGORITMA MOBILENETV2 UNTUK KLASIFIKASI HURUF HIJAIYAH BERBASIS GESTUR TANGAN Riswan, Muh.; Wahyuni, Titin; Danuputri, Chyquitha; Habi Talib, Emil Agusalim; Faisal, Muhammad; Anas, Lukman; Agung, Andi
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.535

Abstract

The digitalization of religious education offers significant opportunities to enhance Hijaiyah letter learning, particularly for the hearing-impaired community through visual gesture recognition. This study aims to develop and evaluate a real-time web-based classification system for 28 Hijaiyah hand gestures using the MobileNetV2 architecture. The research methodology involves a quantitative approach utilizing transfer learning with a balanced dataset of augmented images. The model was trained using fine-tuning techniques and deployed on a web platform using TensorFlow.js and MediaPipe for efficient on-device inference. Experimental results demonstrate that the model achieved an overall accuracy of 84% on the independent test set, with specific classes reaching near-perfect detection in real-time scenarios, although misclassification persisted among visually similar gestures. The system effectively balances computational efficiency with classification performance, minimizing latency during user interaction. In conclusion, the implementation of MobileNetV2 facilitates a responsive and accessible educational tool, proving the viability of computer vision in creating inclusive religious learning environments without requiring complex server-side infrastructure.
Student Emotion Recognition from Low-Quality Videos Using Multimodal Deep Learning ANDI MAWADDA TAIBA MAWADDA TAIBA; Rizki Yusliana Bakti; Muhammad Faisal; Muhammad Syafaat S. Kuba; Lukman Anas; Emil Agusalim H. T; Fahrim I. Rahman
JURNAL INFOTEL Vol 18 No 1 (2026): February
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v18i1.1523

Abstract

Emotion recognition plays a critical role in intelligent e-learning systems by enabling adaptive feedback and timely pedagogical interventions based on students’ affective states. However, most existing approaches rely heavily on visual facial cues, which are highly vulnerable to real-world conditions such as low-resolution video, partial facial occlusion, poor lighting, and unstable network connections commonly encountered in online learning environments. These limitations significantly degrade the performance of unimodal deep learning models. To address this challenge, this study proposes a multimodal deep learning framework for student emotion recognition that is robust to low-quality and occluded video input. The proposed model integrates visual and audio modalities through a hybrid architecture, combining a lightweight CNN-based visual feature extractor with a BiLSTM-based speech emotion model. An attention-based fusion mechanism is employed to adaptively weight cross-modal features, allowing the system to compensate for degraded or missing visual information using complementary acoustic cues. Experimental evaluations are conducted using publicly available datasets representative of realistic online learning scenarios, including DAiSEE and RAVDESS, with additional augmentation to simulate varying levels of occlusion and video degradation. The results demonstrate that the multimodal approach consistently outperforms unimodal baselines, particularly under high occlusion conditions, while maintaining computational efficiency suitable for near real-time deployment. These findings confirm that multimodal fusion with attention mechanisms provides a more resilient and practical solution for emotion-aware e-learning systems operating under non-ideal input conditions
Optimasi Kinerja Arsitektur CNN Ringan Menggunakan Pendekatan Bayesian untuk Identifikasi Skrip Bima Dayang Aisyah; Muhammad Faisal; Lukman Anas; Abd Rakhim Nanda; Syadiah Nor Wan Shamsuddin; Muhammad Syafaat S. Kuba
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 7, No 2 (2026)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v7i2.3462

Abstract

Identifikasi aksara daerah penting untuk mendukung pelestarian budaya digital, namun masih terkendala keterbatasan dataset, kemiripan karakter, dan kebutuhan model yang efisien. Penelitian ini mengoptimasi arsitektur Lightweight CNN menggunakan Bayesian Optimization untuk identifikasi aksara Bima. Dataset terdiri atas 6.190 citra aksara Bima dalam 44 kelas, mencakup aksara Bima baru dan lama. Model menggunakan MobileNetV3-Large sebagai backbone dengan optimasi learning rate, dropout, batch size, dan konfigurasi fine-tuning melalui Tree-structured Parzen Estimator. Hasil eksperimen menunjukkan accuracy 93,06%, precision 92,26%, recall 92,55%, dan F1-score 91,91%, lebih unggul dibanding machine learning tradisional, CNN konvensional, dan beberapa CNN ringan modern. Target accuracy 90% dicapai pada trial keempat. Dengan 3.253.676 parameter dan waktu inferensi 63,35 ms per citra, model ini terbukti akurat, efisien, dan berpotensi diterapkan pada digitalisasi manuskrip serta OCR aksara daerah.
KLASIFIKASI PENYAKIT TANAMAN NILAM BERDASARKAN CITRA DAUN MENGGUNAKAN GLCM DAN SVM Sarina; Rizki Yusliana Bakti; Muhammad Faisal; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study presents a classification model for detecting diseases in patchouli (Pogostemon cablin Benth) leaves using image processing techniques. The method combines Grey Level Co-occurrence Matrix (GLCM) for texture feature extraction and Support Vector Machine (SVM) for classification, optimised using the Particle Swarm Optimisation (PSO) algorithm. A total of 2,080 leaf images were collected and categorized into four classes: healthy, leaf spot, yellowing, and mosaic. Each image was augmented and converted to grayscale to enhance the dataset and reduce computational complexity. Four GLCM features—contrast, correlation, energy, and homogeneity—were extracted to represent leaf textures. The classification model achieved an accuracy of 89.74% using SVM alone, and improved to 97.12% when optimized with PSO. The results indicate that the integration of GLCM, SVM, and PSO provides an effective and accurate solution for early detection of patchouli leaf diseases, potentially supporting farmers in decision-making and improving crop productivity and quality.
IMPLEMENTASI DEEP LEARNING MENGGUNAKAN HYBRID SENTENCE-TRANSFORMERS DAN K-MEANS UNTUK PERBANDINGAN JURNAL Muhammad Asygar Faeruddin; Muhammad Faisal; Rizki Yusliana Bakti; Muhammad Syafaat; Muhyiddin AM Hayat; Andi Makbul Syamsuri; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study addresses the challenge of identifying semantic relatedness between scientific journal articles by developing a classification system based on deep learning. The system applies an unsupervised learning approach using the Sentence-Transformers model and K-Means clustering to generate semantic similarity scores and categorical labels. Abstracts from journal PDFs are extracted and processed to determine similarity levels across four predefined categories. The optimal number of clusters was determined using Elbow Method, Silhouette Score, and Davies-Bouldin Index, resulting in k = 4. The system is implemented as a web-based application that allows users to upload two PDF files, compare them semantically, and receive both a similarity score and an AI-generated narrative explanation. Functional testing showed that all core features performed as expected. This system significantly reduces the time required to assess relatedness between journal articles, offering an efficient tool for academic research navigation.
IMPLEMENTASI K-MEANS DAN ANALISIS SENTIMEN KRITIK SARAN BERBASIS NLP PADA DATA MONEV BBPSDMP KOMINFO MAKASSAR Syahril Akbar; Muhammad Faisal; Rizki Yusliana Bakti; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Manual analysis of large-scale and unstructured textual feedback data is often inefficient and subjective, thereby hindering data-driven decision-making. This study aims to design and implement an integrated analytical workflow to automatically filter, cluster, and classify feedback data consisting of criticisms and suggestions. The research employs a hybrid approach that begins with TF-IDF-based data filtering, followed by dimensionality reduction using Latent Semantic Analysis (LSA), and topic clustering through K-Means clustering optimized with the Silhouette Score. The resulting cluster labels are then used as training data to build a Multinomial Naive Bayes classification model. The results show that this workflow successfully identified two main thematic clusters, namely "Criticism and Expectations" and "Suggestions and Compliments", and the classification model achieved an overall accuracy of 91%. Although class imbalance affected the recall of the minority class (47%), the model demonstrated high precision (95%) for that class. It is concluded that this hybrid approach effectively transforms raw data into structured insights, and utilizing clustering results as training data is an efficient strategy for automating feedback categorization, providing a reliable tool for institutional analysis.
PREDIKSI PEMAKAIAN AIR BULANAN DI PDAM KECAMATAN TAMALATE MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) Nur Annisa Syarifuddin; Titin Wahyuni; Muhammad Faisal; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Water consumption forecasting is a crucial aspect of efficient water resource management, particularly in urban areas with increasing demand. This study aims to predict the monthly water usage volume at the PDAM of Tamalate District using the Autoregressive Integrated Moving Average (ARIMA) method. The dataset consists of historical water usage data from January 2022 to December 2024, totaling 36 monthly observations. The analysis process includes stationarity testing using the Augmented DickeyFuller (ADF) test, model parameter identification through ACF and PACF plots, and performance evaluation using MAE, RMSE, and MAPE metrics. The results show that the best-performing model is ARIMA, which demonstrates high prediction accuracy, with a MAE of 26,049.80 m³, RMSE of 37,459.00 m³, and MAPE of 4.12%. This model is capable of generating predictions close to actual values and can be relied upon as a basis for PDAM’s water distribution planning. It is expected that this research will contribute to data-driven decision-making and support digital transformation in the public service sector.
Co-Authors Abd Halik Lateko Abdul Rakhim Nanda Adi Malik Muhammad Mutsuhito Agung, Andi Ahmad Risal Alfiani, Ananda Andi Agung ANDI AGUNG DWI ARYA BULU Andi Makbul Syamsuri ANDI MAWADDA TAIBA MAWADDA TAIBA Andi Muhammad Akbar Andi Yusri andi Yusri Arvianda Asep Indra Syahyadi Aswad, Muh. Akhwan Adam Bakti, Rizki Yusliana Basir, Basri Chatarina Umbul Wahyuni Chyquitha Danuputri Danuputri, Chyquitha Dayang Aisyah Dian Pramana Putra, Dian Pramana Emil Agus Salim Habi Talib Emil Agusalim H. T Fachrim Irhamna Rachman Fahmi Ramadhan S Fahrim I. Rahman Fahrim Irhamna Rachman Firman Firman Furqan Zakiyabarsi Herdiansyah Herdiansyah Indriyanti Azis Irma Andriani Kasmiati Kasmiati Kazman Riyadi Lukman M a h a r i d a Muh. Akhwan Adam Aswad Muh. Nasrum Massi Muh. Riswan Muhammad Asygar Faeruddin Muhammad Faisal Muhammad Syafaat S. Kuba Muhsin, Arief Muhsin, Muh Arief Muhyiddin A.M Hayat Mujadilah, Siti Muniardi Muniardi Muniardi, Muniardi Nadir Nadir Namora, Namora Nandy Rizaldy Najib Nur Alam Nur Alam Nur Annisa Syarifuddin Nur Fadillah Sari Rahman Rahman Rahman, Erik RAHMANIA Rahmania Rahmaniah, Rahmaniah Rahmi Rahmi Reski Amalia Salam Rezki Nurdin Ridwang Ridwang Ridwang Ridwang Ridwang, Ridwang Riswan, Muh. Saleh, Syafiuddin Sarina Setiawan, Tommy Reynaldy Syadiah Nor Wan Shamsuddin SYAFAR, A. MUHAMMAD Syafiuddin Saleh Syahril Akbar SYAHYADI, ASEP INDRA TANTRI INDRABULAN Titin Wahyuni Wahyuddin Wiwin Fuad Sanjaya