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Sitti Arni
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jurnalprogres@gmail.com
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JL A.P Petarani No. 27 Panakukan Makassar
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INDONESIA
Jurnal Informatika Progres
Published by STMIK PROFESIONAL MAKASSAR
ISSN : 20868359     EISSN : 2797622X     DOI : https://doi.org/10.56708/progres.v14i1.300
Core Subject : Science,
Computer Science & IT
Jurnal Informatika Progres merupakan jurnal Blind Peer-Review yang dikelola secara profesional dan diterbitkan oleh P3M STMIK Profesional Makassar dalam upaya membantu peneliti, akademisi, dan praktisi untuk mempublikasikan hasil penelitiannya. Jurnal ini didedikasikan untuk publikasi hasil penelitian dalam bidang yang memuat artikel tentang Teknologi, Komunikasi, Informasi dan Komputer. Terbit dua kali setiap tahun, 2 nomor 1 volume, yaitu pada bulan April dan September. Semua publikasi di Jurnal Informatika Progres ini bersifat akses terbuka yang memungkinkan artikel tersedia secara online tanpa berlangganan apapun.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 207 Documents
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ANALISIS PERAMALAN KLAIM TABUNGAN HARI TUA MENGGUNAKAN METODE ARIMA PADA PT. ASABRI CABANG MAKASSAR Issan; Indri Mita Pagasing; Andi Harmin; Sitti Arni
PROGRESS Vol 17 No 1 (2025): April
Publisher : P3M STMIK Profesional Makassar

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Abstract

The Autoregressive Integrated Moving Average (ARIMA) method was used in this study to forecast the number of Old Age Savings (THT) insurance claims at PT ASABRI (Persero) Makassar Branch. The data used consisted of 37 monthly observations of THT claims from May 2022 to May 2025. The model identification results indicate that the ARIMA (1,1,0) model is appropriate, with a p-value <0.05 and residuals similar to white noise. This forecast was made for June to December 2025. According to the Mean Absolute Percentage Error (MAPE) value of 17,7123%, this model has a fairly high level of accuracy. It is hoped that the results of this study will assist businesses in making financial decisions and strategic planning.
KLASIFIKASI TINGKAT KEMATANGAN LADA MENGGUNAKAN ENSEMBLE LEARNING BERDASARKAN CITRA WARNA KULIT Jihan Izzathul Mujidah; Rizki Yusliana Bakti; Lukman; Muhammad Faisal; Muhammad Syafaat; Muhyiddin AM Hayat; Andi Makbul Syamsuri
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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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; 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

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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

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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

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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

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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.
IMPLEMENTASI HYBRID LEXICON-BASED DAN SVM UNTUK KLASIFIKASI ANALISIS SENTIMEN TERHADAP PELATIHAN BBPSDMP KOMINFO MAKASSAR Nur Alam; 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

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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.
PENERAPAN ALGORITMA K-NEAREST NEIGHBOR DALAM ANALISIS PEMINJAMAN BARANG PADA DIVISI INVENTARIS TVRI MAKASSAR Risal; Chyquitha Danuputri; Darniati; Muhyiddin AM Hayat
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

Inventory management in the TVRI Makassar Inventory Division is inefficient due to the lack of a predictive system, hampering proactive asset requirement planning. This study aims to apply the K-Nearest Neighbor (KNN) algorithm to analyze historical borrowing patterns, predict demand for goods three months in advance, and evaluate model accuracy. Using a quantitative approach, this study implements a systematic machine learning workflow, including data preprocessing, temporal feature engineering, class imbalance handling using the Synthetic Minority Over-sampling Technique (SMOTE), and hyperparameter optimization using GridSearchCV. The results show that the optimized KNN model achieved an overall accuracy of 80.18%, significantly outperforming the baseline model. Key findings revealed that the model's performance is contextual, with very high reliability (F1-Score > 0.95) on frequently borrowed assets, and is able to identify strong temporal demand patterns. It is concluded that KNN is effective for segmented inventory demand prediction and has the potential to serve as a basis for TVRI Makassar to adopt a proactive, datadriven inventory management strategy, enabling more efficient resource allocation.
IMPLEMENTASI METODE PROTOTYPE PADA SISTEM PAYMENT GATEWAY UNTUK SEPATU KOTAMA Nadhira Najmi Hendri Lubis; Tantri Hidayati Sinaga
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

Digital era nowadays, online payment systems have become an essential component in supporting transaction efficiency, especially for enterpreneur such as Toko Sepatu Kotama. The store's current manual payment process causes several issues, including delayed confirmations, risk of data entry errors, and a lack of convenience for customers. Therefore, this study aims to develop a web-based payment gateway system that is secure, efficient, and easy to use. The development method used is the prototype model, which enables iterative processes involving users to ensure that the system meets actual needs. The system is built using the Laravel framework and MySQL, and integrated with Midtrans as the payment service provider. The interface is designed to be responsive for both desktop and mobile devices.Testing using Black Box Testing shows that all main features function properly, including registration, login, order placement, checkout, and payment processing. Admins can also effectively manage products, orders, and sales reports. The system's advantages include real-time payment notifications and direct integration with Midtrans. The system has proven to improve operational efficiency and user convenience. Future development may include automatic shipping tracking, multilingual support, and more diverse payment options such as credit cards and installment plans. It is also recommended to conduct broader user testing to gain more representative feedback from the market.
PENGEMBANGAN APLIKASI SMARTSCHOOL BERBASIS WEB UNTUK ADMINISTRASI SEKOLAH DASAR DENGAN AGILE SCRUM Rizky Budi Ramdhani; Ahmad Zakir
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

The development of information technology has encouraged many educational institutions to adopt digital systems in order to improve the efficiency and accuracy of school administration management. However, at SD Qurratu A’yun, administrative processes such as recording student data, teacher data, and financial transactions are still carried out manually, leading to various problems such as data loss, reporting delays, and inconsistent information. Based on a case study at SD Qurratu A’yun, a web-based school administration information system was developed as a digital solution. The system was designed using the Agile method with a Scrum approach, enabling iterative development that is responsive to user needs. The development process involved several stages: product backlog planning, Sprint planning, Sprint backlog development, daily Scrum meetings, Sprint review, and Sprint retrospective. The system is equipped with key features such as teacher, student, and class data management, transaction type management, cash transactions, and financial reports accessible by academic year. System testing showed that the developed solution successfully improved data organization, accelerated reporting processes, and provided ease of access to information in real time for administrators, treasurers, and principals. By applying the Agile (Scrum) method and building a web-based system, this research provides an optimal solution to the challenges of manual administration at SD Qurratu A’yun. The system not only streamlines workflows but also supports the sustainability of school operations in a more efficient and structured manner.

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