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S. Kuba, Muhammad Syafa'at
Teknik Pengairan, Fakultas Teknik, Universitas Muhammadiyah Makassar
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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 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.
Pengaruh Jarak Struktur Pemecah Gelombang Model Hybrid Engineering Terhadap Panjang Dan Tinggi Gelombang (Penelitian Laboratorium) Zulmi, Ahmad Syafi'i; Kato, Muh Alvin Achmad; Karamma, Riswal; Kuba, Muhammad Syafa'at S.
Journal of Muhammadiyah’s Application Technology Vol. 1 No. 2 (2022)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/jumptech.v1i2.8925

Abstract

Hybrid Engineering dibangun dengan menggunakan bahan-bahan yang tersedia secara local seperti kayu, bambu, ranting, dan dahan pohon mangrove. Penelitian ini bertujuan untuk mengetahui nilai refleksi dan transmisi gelombang pada model pemecah gelombang model hybrid engineering, kemudian menganalisi parameter-parameter yang berpengaruh terhadap koefisien refleksi dan transmisi pada pemecah gelombang. Penelitian ini dilakukan di Laboratorium Hidrolika Teknik Sipil Fakultas Teknik Universitas Hasanuddin. Metode yang digunakan berbasis eksperimental. Karakteristik gelombang yang dihasilkan terdiri dua variasi priode dan dua variasi kedalaman air serta dua variasi stroke. Pembacaan puncak dan lembah gelombang dilakukan secara otomatis melalui wave monitor. Dari hasil penelitian dapat disimpulkan semakin besar jarak struktur pemecah gelombang maka gelombang refleksi akan semakin kecil sedang untuk gelombang transmisi yang dihasilkan akan semakin tinggi.
Penerapan Model Geostudio untuk Analisis Stabilitas Bendungan Karalloe Juandani, Anis Dandi; Syah, Akrar; Kuba, Muhammad Syafaaat S; Djunur, Lutfi Hair
Journal of Muhammadiyah’s Application Technology Vol. 2 No. 1 (2023)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/jumptech.v2i1.9375

Abstract

Bendungan merupakan salah satu bangunan air yang dibangun dengan tujuan untuk menampung, menampung laju air kemudian menyimpannya sehingga menjadi waduk atau danau. Penelitian ini bertujuan untuk mengetahui stabilitas pada bangunan pada kondisi setelah konstruksi berdasarkan analisis menggunakan program GeoStudio 2018. Metode yang digunakan adalah Fellenius dengan program software Geostudio Slope/W 2018 untuk menganalisis stabilitas lereng dan rembesan pada bendungan tubuh di Karalloe Kabupaten Gowa. Dari hasil analisis stabilitas sedimen terhadap rembesan pada kondisi muka air banjir bagian hulu diperoleh angka keamanan sebesar 2,466 dan radius kritikal sebesar 11,835 sedangkan total rembesan 1,994x10 -3 m 3 /dtk. Dan bagian bawah diperoleh angka keamanan sebesar 1,828 dan radius kritisal sebesar 11,865. Dengan menggunakan pemodelan Software Geostudiao 2018 dinyatakan aman dan memenuhi syarat.
Analisa Debit Rancangan pada Das Sungai Tangka Kabupaten Sinjai Arsyad, Zulfikar; Mahmud, Rajib; Agusalim, Agusalim; Kuba, Muhammad Syafa’at S
Journal of Muhammadiyah’s Application Technology Vol. 2 No. 2 (2023)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/jumptech.v2i2.12642

Abstract

DAS (daerah aliran sungai) merupakan suatu wilayah daratan yang merupakan satu kesatuan sungai dan anak-anak sungainya. Yang berfungsi menampung, menyimpan, dan mengalirkan air yang berasal dari curah hujan ke danau dan ke lautan secara alami, yang batas di darat merupakan pemisah topografi dan batas laut sampai dengan daerah perairan yang masih terpengaruh aktivitas daratan. Tujuan dari penelitian ini adalah untuk mengetahui seberapa besar debit aliran Sungai TangkaKabupaten Sinjai. Dalam penelitian ini digunakan tiga metode untuk mengetahui debit banjir rancangan pada DAS sungai tangka, diantaranya metode perhitungan HSS Nakayasu, metode rasional dan metodehaspers. Hasil debit banjir  rancangan periode ulang 2, 5, 10, 20, 25, 50, dan 100 tahun pada ketiga metode meningkat seiring dengan meningkatnya tahun. Debit rancangan tertinggi yaitu pada metode HSS Nakayasu dengan nilai kala ulang 2 tahun sebesar 856,8, dan metode rasional sebesar 403,9, dan metode haspers sebesar 168,7 Kata kunci debit sungai, periode ulang, HSS Nakayasu,metode rasional, metode haspers
Tinjauan Perencanaan Check Dam Bonto Cani Kab. Bone Provinsi Sulawesi Selatan Gemilang, Berni Satria; Munawir, Mohamad; Nurnawaty, Nurnawaty; Latief, Fausiah; Kuba, Syafa’at S.; Mahmuddin, Mahmuddin; Anas, Andi Bunga Tongeng
Journal of Muhammadiyah’s Application Technology Vol. 1 No. 1 (2022)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/jumptech.v1i1.7315

Abstract

Check Dam atau Dam Penahan adalah suatu bangunan yang dibangun di lembah sungai yang cukup dalam untuk menahan, menampung dan mengendalika sedimen agar jumlah sedimen yang mengalir menjadi lebih kecil atau sebagai sarana untuk usaha melestarikan sumber-sumber air dan pengendalian sedimen. Tujuan dari penelitian ini yaitu untuk mengetahui besar debit banjir rencana yang terdapat pada check dam Bonto Cani dan mengetahui kondisi eksisting bangunan check dam Bonto Cani.Langka awal dalam perencanaan check dam ini yaitu analisis hidrologi untuk menentukan debit banjir rencana digunakan 3 stasiun data pencatatan curah hujan, dimana stasiun Pallattae, stasiun Camba, dan stasiun Malino, dengan masing-masing data yang digunakan 30 tahun dimulai dari tahun 1991 sampai tahun 2020. Hasil analisa debit banjir rencana selanjutnya digunakan untuk analisis hidrolis check dam dan struktur check dam yang meliputi tinggi Main dam, panjang lantai, dan Subdam. Luas DAS Bonto Cani 459,26 km2, Panjang sungai Utama ± 69,169 km
Co-Authors Ade Irfan Agus, Fauziah Agusalim, Agusalim Agusalim, M. Ahmad Syafi'i Zulmi Akbar, Syahril Akrar Syah Al Imran, Hamzah Ali, Muhammad Yunus Amal, Citra Amalia Amrullah Anas, Andi Bunga Tongeng Andi Bunga Tongeng Anas Andi Rahmat Anis Dandi Juandani Antaria, Sukmasari Arman, Muayyanah Arsyad, Zulfikar Asnita Virlayani, Asnita Bakti, Rizki Yusliana Berni Satria Gemilang Djunur, Lutfi Hair Faeruddin, Muhammad Asygar Fausiah Latief Fauzan Hamdi Fithriyah Arief Wangsa Gaffar, Farida Gemilang, Berni Satria Hamzah Al Imran Hasanuddin, Novianingsih Irma Suryana Irwan Irwan, Muhammad Ahlil Khairi Juandani, Anis Dandi Juliandro, Juliandro Karim, Nenny Kasmawati Kato, Muh Alvin Achmad Lantara, Andi Bintang Latief, Fausiah Lisnawati Lisnawati LUKMAN ANAS Lukman Lukman Lutfi Hair Djunur Ma'rupah, Ma'rupah Mahmud, Rajib Mahmuddin Mahmuddin Mahmuddin Mohamad Munawir Muh Alvin Achmad Kato Muhammad Aminuddin, Muhammad Muhammad Faisal Muhyiddin A.M Hayat Mujidah, Jihan Izzathul Munawir, Mohamad Nenny Nenny Nenny Nenny, Nenny Nini Apriani Rumata Nur Alam Nurdiansah, Nurdiansah Nurnawaty Nurnawaty Nurnawaty, Nurnawaty Panguriseng, Darwis Pawara, Ismail Putri, Adriani Rahmasari, St. Rajib Mahmud Risman, Andi Muh. Riswal Karamma Riswal Karamma Sahril Sandi, Andi Muhammad Sarina Siba, Ikhsan Syah, Akrar Syahrul, Syahrulrahman Syamsuri, Andi Makbul Syamsuri, Andi Maqbul Syarifuddin, Nur Annisa T Karim, Nenny Taufiq, Muh Titin Wahyuni Toha Andi Lala Usman, Sucipto Wangsa, Fithriyah Arief Zulfikar Arsyad Zulhaidir DJ, Muhammad Zulmi, Ahmad Syafi'i