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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) Jurnal Sisfokom (Sistem Informasi dan Komputer) 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 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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IMPLEMENTASI K-MEANS DAN ANALISIS SENTIMEN KRITIK SARAN BERBASIS NLP PADA DATA MONEV BBPSDMP KOMINFO MAKASSAR Akbar, Syahril; Faisal, Muhammad; Bakti, Rizki Yusliana; Syafaat, Muhammad; Syamsuri, Andi Makbul; AM Hayat, Muhyiddin; Anas, Lukman
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i2.465

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.
IMPLEMENTASI DEEP LEARNING MENGGUNAKAN HYBRID SENTENCE-TRANSFORMERS DAN K-MEANS UNTUK PERBANDINGAN JURNAL Faeruddin, Muhammad Asygar; Faisal, Muhammad; Bakti, Rizki Yusliana; Syafaat, Muhammad; AM Hayat, Muhyiddin; Syamsuri, Andi Makbul; Anas, Andi Lukman
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i2.466

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.
KLASIFIKASI PENYAKIT TANAMAN NILAM BERDASARKAN CITRA DAUN MENGGUNAKAN GLCM DAN SVM Sarina; Bakti, Rizki Yusliana; Muhammad Faisal; Muhammad Syafaat; Syamsuri, Andi Makbul; AM Hayat, Muhyiddin; Anas, Andi Lukman
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i2.469

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.
PREDIKSI PEMAKAIAN AIR BULANAN DI PDAM KECAMATAN TAMALATE MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) Syarifuddin, Nur Annisa; Wahyuni, Titin; Faisal, Muhammad; Syafaat, Muhammad; Syamsuri, Andi Makbul; AM Hayat, Muhyiddin; Anas, Andi Lukman
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i2.471

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 Dickey-Fuller (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.
IMPLEMENTASI HYBRID LEXICON-BASED DAN SVM UNTUK KLASIFIKASI ANALISIS SENTIMEN TERHADAP PELATIHAN BBPSDMP KOMINFO MAKASSAR Alam, Nur; Faisal, Muhammad; Bakti, Rizki Yusliana; Syafaat, Muhammad; Syamsuri, Andi Makbul; AM Hayat, Muhyiddin; Anas, Andi Lukman
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i2.473

Abstract

The evaluation of government training programs is often hindered by manual analysis of unstructured qualitative feedback, making the process inefficient and subjective. This study aims to implement and evaluate a sentiment classification model using a hybrid Lexicon-Based and Support Vector Machine approach to analyze participants’ perceptions of the Vocational School Graduate Academy training organized by BBPSDMP Kominfo Makassar, as well as to compare the performance of a standard SVM model with a model optimized using Particle Swarm Optimization. This quantitative research employs 2,313 unstructured review data, which undergo text preprocessing, initial lexicon-based labeling, and TF-IDF feature extraction before being classified using an SVM with an RBF kernel. The results show that the SVM model optimized with PSO consistently outperforms the standard model across all four evaluation aspects, with the most significant accuracy improvement observed in the instructor category from 84.71% to 89.02% and in the assessor category reaching 91.46%. PSO optimization has proven effective in enhancing the model’s ability to identify negative sentiments, which represent the minority class. The hybrid approach with PSO optimization is capable of producing a more accurate and balanced classification system, with practical implications as an objective automated evaluation tool.
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.
Peningkatan Keterampilan Kelompok Julu Atia melalui Pelatihan Pembuatan Nugget Rumput Laut Kasmiati, Kasmiati; Andriani, Irma; Massi, Muh. Nasrum; Rahmi, Rahmi; Anas, Lukman; Zakiyabarsi, Furqan
Jurnal ABDINUS : Jurnal Pengabdian Nusantara Vol 10 No 1 (2026): Volume 10 Nomor 1 Tahun 2026
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/ja.v10i1.24591

Abstract

Seaweed is a leading commodity in the fisheries sector that has not been optimally utilized, particularly by community groups, to increase its added value. The problem faced by these groups is a lack of knowledge and skills in processing and selling seaweed-based products. This activity aims to improve the knowledge and skills of "Julu Atia" partners in the production and marketing of seaweed nuggets. The method used was to directly involve 20 members in training and mentoring activities. The training included counseling and practical training on nugget production and marketing, while the mentoring aimed to evaluate the group's ability to independently produce and market the product. Initial knowledge and skills of partners were determined through pre- and post-tests. The results showed that partner knowledge was relatively low, with an average of 34.5%, increasing by 55% to 89.5% after participating in the community service activity. Through nugget production, the added value increased by IDR 222,000 per kg of seaweed. Thus, this activity effectively increased the empowerment level of "Julu Atia" and can be replicated in other groups to improve the welfare of coastal communities.
Predictive Models Talented Researcher Using Modern Approach Quantum Machine Learning (QML) Anas, Lukman; Sofwan, Aghus; Setiawan, Iwan
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2593

Abstract

Scientific advancement is profoundly shaped by the ability of exceptional individuals to investigate and produce novel insights. Nonetheless, conventional assessment techniques that depend on bibliometric metrics such as the Scopus Score, Sinta, Google Scholar (GS) , and the H-Index—frequently neglect to encapsulate the intricate dynamics associated with research quality. In order to rectify these inadequacies, this study introduces a model aimed at the identification of gifted researchers through a Quantum Machine Learning (QML) methodology. The proposed framework seeks to surmount the constraints of ranking systems that rely exclusively on scientometric indicators by incorporating a reconstructed kernel Hilbert space (RKHS). The research methodology is delineated into four principal phases: (1) data gathering and preprocessing, (2) QSVM model training, (3) researcher score identification and visualization, and (4) performance assessment by comparing actual and anticipated scores. QSVM was tested using a dataset of researchers from various fields. Results show that QSVM accurately predicts researcher performance, with variances between Scores for the whole thing range from -0.25 to 0.05. The plan that was offered congruence with actual performance data supports its robustness. The ranking analysis shows a low mistake rate, proving QSVM's academic performance evaluation accuracy. QML-based categorization models can be scalable and data-driven alternatives to standard research assessment methods, according to this study.
Co-Authors A.MUHAMMAD SYAFAR Abd Halik Lateko Adi Malik Muhammad Mutsuhito Aghus Sofwan Agung, Andi Ahmad Risal Akbar, Syahril Alfiani, Ananda ANDI AGUNG DWI ARYA BULU Andi Muhammad Akbar andi Yusri Andi Yusri Arvianda Asep Indra Syahyadi Aswad, Muh. Akhwan Adam Bakti, Rizki Yusliana Basir, Basri Chatarina Umbul Wahyuni Danuputri, Chyquitha Dian Pramana Putra, Dian Pramana Fachrim Irhamna Rachman Faeruddin, Muhammad Asygar Fahmi Ramadhan S Firman Firman Furqan Zakiyabarsi Habi Talib, Emil Agusalim Herdiansyah Herdiansyah Irma Andriani Iwan Setiawan Kasmiati Kasmiati Kazman Riyadi Lukman M a h a r i d a Muh. Akhwan Adam Aswad Muh. Nasrum Massi Muhammad Faisal Muhammad Faisal 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 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. S. Kuba, Muhammad Syafa'at Saleh, Syafiuddin Sarina Setiawan, Tommy Reynaldy Syafiuddin Saleh SYAHYADI, ASEP INDRA Syamsuri, Andi Makbul Syarifuddin, Nur Annisa TAIBA, ANDI MAWADDA TAIBA MAWADDA TANTRI INDRABULAN Titin Wahyuni Wahyuddin