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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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IMPLEMENTASI SISTEM PENDAFTARAN SISWA BARU BERBASIS WEB MENGGUNAKAN ALGORITMA SAW DI SANGGAR KEGIATAN BELAJAR UJUNG PANDANG Calvin Bonar Sarumpaet; Hidayatul Fajri; Suardi Hi Baharuddin; Dikwan Moeis
PROGRESS Vol 17 No 1 (2025): April
Publisher : P3M STMIK Profesional Makassar

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

Abstract

The new student registration process at Sanggar Kegiatan Belajar (SKB) Ujung Pandang was previously conducted manually, resulting in several issues such as data processing delays, file accumulation, and lack of transparency in selection. This study aims to develop a web-based registration information system integrated with the Simple Additive Weighting (SAW) algorithm to enhance the efficiency and objectivity of the selection process. The system was developed using the Waterfall model and implemented using PHP and MySQL-based web technology. The implementation results show that the system can automate registration and selection in real-time. The SAW algorithm effectively produces objective participant rankings based on criteria such as exam scores, age, and domicile. Evaluation indicates that the system improves selection speed, result accuracy, and facilitates data management for users. It can be concluded that this system provides significant benefits for both SKB administrators and applicants and is relevant in supporting the digital transformation of non-formal education.
IMPLEMENTASI MODEL BUSINESS TO BUSINESS PADA PEMASARAN PRODUK JAMU MENGGUNAKAN FRAMEWORK LARAVEL Arya Dwi Wahyud; Boni Oktaviana Sembiring; Sabrina Aulia Rahmah
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

The advancement of digital technology presents great opportunities for business actors to enhance their competitiveness through more efficient marketing strategies. This study aims to implement the Business to Business (B2B) model in the marketing of traditional herbal products by Jamu Dapoer Niswah using the Laravel Framework and the Agile method. The system developed is a web-based platform with core features such as business partner registration, product catalog, ordering, order tracking, and contract management. The Agile method was chosen for its flexibility in responding to changing user needs through iterative development. This study adopts an iterative approach consisting of planning, implementation, testing, documentation, deployment, and maintenance stages. The implementation results show that the system is capable of accelerating the ordering process, improving transaction accuracy, and expanding the partner market reach. System testing was conducted using the black-box method and showed that all features function as intended. With this system, the marketing and distribution processes of herbal products can be carried out in a more structured and efficient manner, accessible to business partners at any time. This system is expected to become a digital solution that supports B2B marketing transformation in the traditional business sector.
KLASIFIKASI TANAMAN OBAT TRADISIONAL BERBASIS CITRA BUAH DAN DAUN Nurul Kusumawardani; Chyquitha Danuputri; Darniati; Muhammad Faisal; Muhyiddin A.M Hayat; Muhammad Syafaat S.Kuba; Desi Anggreani
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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Abstract

Indonesia is a megabiodiversity country with extensive use of traditional medicinal plants; however, plant identification in natural environments remains largely manual and error-prone. Recent advances in deep learning, particularly Vision Transformer (ViT), provide a promising solution by effectively capturing global spatial features for image classification. This study applies a ViT-Base/16 model to automatically classify fruit and leaf images of Indonesian medicinal plants. The dataset comprises 1,000 field-collected images from Galung Village, West Sulawesi, covering 20 classes (10 medicinal and 10 non-medicinal plants). The model was fine-tuned using the AdamW optimizer with a learning rate of 2×10⁻⁵ and trained for 30 epochs with cosine annealing. The proposed approach achieved high performance, with 99.33% accuracy, 99.41% precision, 99.33% recall, and a 99.33% F1-score, while binary classification between medicinal and non-medicinal plants reached 100% accuracy. The system was deployed as a Flask-based web application, demonstrating reliable functionality and practical response times. Overall, the results confirm the effectiveness of Vision Transformer for medicinal plant classification under natural conditions and highlight its potential to support digital documentation, education, and the preservation of local ethnobotanical knowledge.
IMPLEMENTASI SISTEM PENDAFTARAN SISWA BARU BERBASIS WEB MENGGUNAKAN ALGORITMA SAW DI SANGGAR KEGIATAN BELAJAR UJUNG PANDANG Sarumpaet, Calvin Bonar; Fajri, Hidayatul; Baharuddin, Suardi Hi
PROGRESS Vol 17 No 1 (2025): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i1.464

Abstract

The new student registration process at Sanggar Kegiatan Belajar (SKB) Ujung Pandang was previously conducted manually, resulting in several issues such as data processing delays, file accumulation, and lack of transparency in selection. This study aims to develop a web-based registration information system integrated with the Simple Additive Weighting (SAW) algorithm to enhance the efficiency and objectivity of the selection process. The system was developed using the Waterfall model and implemented using PHP and MySQL-based web technology. The implementation results show that the system can automate registration and selection in real-time. The SAW algorithm effectively produces objective participant rankings based on criteria such as exam scores, age, and domicile. Evaluation indicates that the system improves selection speed, result accuracy, and facilitates data management for users. It can be concluded that this system provides significant benefits for both SKB administrators and applicants and is relevant in supporting the digital transformation of non-formal education.
PENERAPAN METODE SMART DALAM PEMILIHAN PERPUSTAKAAN TINGKAT SMP TERBAIK DI KOTA MAKASSAR Ichsan Jaylani; Muhammad Fitrah Rivan; Andi Harmin; Asri Yadi
PROGRESS Vol 17 No 1 (2025): April
Publisher : P3M STMIK Profesional Makassar

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Abstract

The library plays a crucial role in supporting education and literacy. However, determining the best library in Makassar, particularly at the junior high school level, remains challenging due to various qualitative factors. This research applies the Simple Multi Attribute Rating Technique (SMART) method to evaluate and rank libraries objectively based on seven criteria: book collection, facilities, service, accessibility, innovation and technology, literacy activities, and cleanliness and safety. Data was collected through observations, questionnaires, and interviews across selected libraries in Makassar. The system developed provides a decision support tool that calculates weighted scores and recommends the best-performing library. Results show that SMART effectively delivers transparent and structured decision-making to support library quality assessment and policy recommendations.
IMPLEMENTASI ALGORITMA APRIORI UNTUK ANALISIS PERSEDIAAN MATERIAL DI WAREHOUSE PT. TELKOM AKSES MAKASSAR Wal Ikram, Dzul Jalali; Sadrin, Ahmad Rifai; Moeis, Dikwan; Rosnani
PROGRESS Vol 17 No 1 (2025): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i1.453

Abstract

This research aims to implement Apriori algorithm for data mining in material inventory management at PT. Telkom Akses Makassar. Apriori algorithm identifies frequent itemsets and generates association rules from transaction data to optimize warehouse stock management. The methodology includes data collection through observation, interviews, and historical transaction datasets. Data processing uses Apriori to calculate support, confidence, and lift metrics. The results indicate that frequent item combinations can improve planning accuracy and reduce stockouts. A web-based application, Material Analyzer, was developed for analysis and visualization, featuring dashboard, analysis, history, and visualization modules. This study contributes practically by supporting logistics decision-making and theoretically by expanding data mining applications in inventory systems.
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.

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