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All Journal International Journal of Advances in Applied Sciences PROtek : Jurnal Ilmiah Teknik Elektro Ainet : Jurnal Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma, Logika dan Komputasi Jurnal Informatika Progres Mechatronics Journal in Professional and Entrepreneur (MAPLE) ARUS JURNAL SAINS DAN TEKNOLOGI Journal of Muhammadiyah’s Application Technology
Muhyiddin A.M Hayat
Informatika, Universitas Muhammadiyah Makassar
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Penerapan Data Mining untuk Memprediksi Penjualan Bahan Bangunan (Ud Kiki Fatmala) Dengan Menggunakan Metode Naive Bayes Hayat, Muhyiddin A M; Wahyuni, Titin; Haidul, Haidul
Arus Jurnal Sains dan Teknologi Vol 3 No 1: April (2025)
Publisher : Arden Jaya Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57250/ajst.v3i1.1213

Abstract

Penjualan bahan bangunan merupakan bidang usaha yang memiliki prospek menjanjikan dengan permintaan yang terus meningkat seiring pertumbuhan populasi dan pembangunan. Namun, permasalahan yang dihadapi oleh toko bahan bangunan, khususnya UD KIKI FATMALA, adalah kurangnya pemahaman terhadap minat pembeli, yang dapat menyebabkan penumpukan stok barang dan memperlambat perputaran modal. Untuk mengatasi permasalahan ini, penelitian ini menerapkan metode Naïve Bayes untuk memprediksi penjualan bahan bangunan berdasarkan data historis. Penelitian ini bertujuan untuk menggunakan teknik data mining sebagai pendukung keputusan dalam menentukan barang yang memiliki potensi penjualan tinggi guna mengoptimalkan stok dan meningkatkan efisiensi penjualan. Metode penelitian mencakup pengumpulan data penjualan, preprocessing data, pembagian data menjadi training dan testing, serta penerapan algoritma Naïve Bayes. Model yang dibangun dievaluasi menggunakan confusion matrix untuk menilai tingkat akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa metode Naïve Bayes mampu memprediksi penjualan bahan bangunan dengan akurasi sebesar 84%. Implementasi model ini dilakukan menggunakan Microsoft Excel untuk pengolahan data awal dan Jupyter Notebook untuk analisis lebih lanjut. Kesimpulan dari penelitian ini adalah bahwa metode Naïve Bayes dapat digunakan secara efektif untuk memprediksi produk yang memiliki peluang besar untuk terjual, sehingga membantu pengelolaan stok yang lebih optimal di UD KIKI FATMALA
Classification Of Student Mental Health Based On Academic And Social Variables Using The Decision Tree Method Anggreani, Desi; Danuputri, Chyquitha; Hayat, Muhyiddin A M; Setiawan, Dedi
Jurnal Algoritma, Logika dan Komputasi Vol 8, No 1 (2025): Jurnal ALU, Maret 2025
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v8i1.8652

Abstract

Mental health problems are suffered by many people, including students who often have poor lifestyles. Depression and anxiety are widespread among students, with all universities reporting students with depression and 75.5% reporting students with severe anxiety. This research aims to determine the classification of student mental health based on academic and social by using the Decision Tree method so that early treatment can be carried out. The dataset used consists of 11 aspects concerning academic and social. The data that has been collected is processed through the preprocessing stage and analyzed using the Decision Tree classification method. The classification results showed that out of 973 students who did not suffer from depression, the method classified them correctly. In addition, of the 104 college students who were classified as suffering from major depression, all of them were actually suffering from major depression. The agreement between the classification results and the actual condition shows the reliability of this method, with an accuracy rate of 76.71%. This research underscores the importance of academic and social variables in influencing students' mental health. The findings confirm the reliability of the Decision Tree method in detecting students' mental state and point to the need for effective counseling services and mental health interventions in campus and social environments. 
CONGESTION-PRONE POINT CLASSIFICATION SYSTEM USING SOM METHOD ANDROID-BASED A M Hayat, Muhyiddin
Jurnal Algoritma, Logika dan Komputasi Vol 8, No 1 (2025): Jurnal ALU, Maret 2025
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v8i1.8764

Abstract

Urban traffic congestion has emerged as a significant challenge, primarily driven by rapid urban expansion and increasing vehicle usage. This study presents the development of a congestion-prone point classification system utilizing the Self-Organizing Maps (SOM) algorithm, integrated into an android-based mobile application. The primary objective is to facilitate the real-time detection and visualization of traffic density hotspots using unsupervised machine learning techniques. Traffic-related data comprising vehicle volume, type distribution, and geospatial coordinates are systematically collected, preprocessed, and transformed into multidimensional feature vectors. These vectors are processed using the SOM algorithm to uncover latent congestion patterns across various road segments. Testing results indicate that the proposed model is capable of accurately identifying congestion-prone areas, which are subsequently visualized within the mobile application using a colour-coded map interface. This integration provides commuters and traffic management authorities with actionable, data-driven insights to support route optimization and congestion alleviation strategies. Overall, the proposed system contributes to the advancement of intelligent transportation infrastructure within the broader framework of smart city development.
PREDIKSI PENJUALAN OBAT MENGGUNAKAN MODEL LSTM DAN ANALISIS TIME SERIES PADA DATA TRANSAKSI PASIEN BPJS Basyar, Muhammad Yusuf; HAYAT, MUHYIDDIN A. M.; RAHMAN, FAHRIM IRHAMNA
Mechatronics Journal in Professional and Entrepreneur. Vol 7 No 2 (2025)
Publisher : Politeknik Bosowa

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

Abstract

Prediksi penjualan obat yang akurat diperlukan untuk meningkatkan efisiensi persediaan dan kualitas pelayanan kesehatan, terutama di PT Kimia Farma Apotek (KFA) yang melayani pasien BPJS Kesehatan. Permintaan obat yang fluktuatif dan pola penjualan yang kompleks menjadi tantangan dalam perencanaan persediaan. Penelitian ini bertujuan untuk memprediksi penjualan obat secara akurat dengan menerapkan teknik data mining menggunakan model Long Short-Term Memory (LSTM). Data dikumpulkan dari transaksi di PT Kimia Farma Apotek (KFA) dan diproses melalui pra-pemrosesan yang mencakup pembersihan data, transformasi menjadi time series, serta pembagian data menjadi 80% data latih dan 20% data uji. Model LSTM dilatih selama 50 epoch menggunakan fungsi aktivasi ReLU dan Softmax pada lapisan output untuk menghasilkan probabilitas prediksi. Hasil penelitian menunjukkan bahwa model LSTM memiliki Mean Squared Error (MSE) sebesar 0.0021 dan akurasi prediksi mencapai 99.79%, menunjukkan kemampuan model dalam menghasilkan prediksi yang mendekati nilai aktual. Namun, R² Score sebesar -0.14 menunjukkan keterbatasan dalam menangkap variasi data yang kompleks. Penelitian ini menunjukkan bahwa model LSTM memiliki potensi besar untuk diterapkan dalam prediksi penjualan obat dan membantu pengambilan keputusan strategis dalam manajemen persediaan. Selain itu, pendekatan ini berkontribusi positif dalam meningkatkan efisiensi perencanaan persediaan obat di PT Kimia Farma Apotek
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 TINGKAT KEMATANGAN LADA MENGGUNAKAN ENSEMBLE LEARNING BERDASARKAN CITRA WARNA KULIT Mujidah, Jihan Izzathul; Bakti, Rizki Yusliana; Lukman; Muhammad Faisal; Muhammad Syafaat; AM Hayat, Muhyiddin; Syamsuri, Andi Makbul
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.v17i2.467

Abstract

Pepper fruit (Piper nigrum L.) is an agricultural commodity whose market value strongly depends on its ripeness level at harvest. Ripeness determination, which is still commonly performed through visual observation, tends to be inaccurate and subjective. This study aims to classify the ripeness level of pepper fruit based on skin color using an ensemble learning approach. The dataset consists of 1,996 pepper fruit images categorized into four ripeness levels unripe, semi ripe, ripe, and overripe. Color features were extracted from the HSV color model using color moment statistics including mean, standard deviation, and skewness. Random Forest and XGBoost models were combined using a soft voting method. The results show that the ensemble model achieved 98.25% accuracy, 98.30% precision, 98.27% recall, and 98.26% F1-score. The ensemble approach proved superior to single models by providing more accurate and stable classification of pepper fruit ripeness.
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.
Co-Authors . Darniati Abd Rahman, Aedah Abrah, Rezkytullah Akbar DB, Andi Muhammad Akbar, Syahril Anggraeni , Desi Anggreani, Desi Bakti, Rizki Yusliana Basyar, Muhammad Yusuf Danuputri, Chyquitha Danuputri, Chyquitha Dedi Setiawan Djalil, Sony Achmad Dzakki Adam, Ahmad Wildan Fachrim Irhamna Rachman Faeruddin, Muhammad Asygar Fahrim Irhmna Rachman Fatimah Azzahra H, chalidah_azzahra00 Hadawina Hadawina Haidul, Haidul Hermanto, Chalidah Azzahrah khairat, arikal LUKMAN ANAS Lukman Lukman Lukman Lukman Muh Nur Aqsal Aminullah Muhammad Faisal Mujidah, Jihan Izzathul Nisha, Khairun Nur Alam Nur, Muhammad Ikhsan Rahman, Fahrim Irhamna RAHMANIA Rahmania RAMLI, ANDI RAODATUL ADAWIYAH Rasyidi, Muhammad Fachri Reski Abbas Ridwang Ridwang Ridwang Ridwang Ridwang, Ridwang Risal S. Kuba, Muhammad Syafa'at Salsabila, Ismi Sarina Sunarti Suriani Suriani Syamsuri, Andi Makbul Syarifuddin, Nur Annisa Titin Wahyuni Usman, Ansharullah Patiroi Utama, Prengki Putra Wahid, Abd Rahman