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All Journal PROtek : Jurnal Ilmiah Teknik Elektro Ainet : Jurnal Informatika Jurnal Algoritma, Logika dan Komputasi Jurnal Informatika Progres
Muhyiddin A M Hayat
Informatika, Universitas Muhammadiyah Makassar
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media

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Analisis Success Full Call Ratio Network Telecommunication pada Global System For Mobile Communication (GSM) PT.Telkomsel RTPO Berau Hadawina Hadawina; Rahmania Rahmania; Ridwang Ridwang; Rizki Yusliana Bakti; Muhyiddin A M Hayat
Ainet : Jurnal Informatika Vol 3, No 2 (2021): September (2021)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/ainet.v3i2.7649

Abstract

This GSM network stands for Global System for Mobile Communications, which is a digital mobile communication technology, especially in mobile phones. Currently, almost all mobile telecommunicaions instrumentation uses cellular-based technology. Cellular-based mobile telecommunications systems offer advantages compared to wireless systems, namely mobility so that users can move anywhere as long as they are still within the operator's service coverage. The purpose of this study was to measure and analyze how the GSM (Global System For Mobile Communications) network performance of a BTS at PT.Telekomunikasi RTPO Berau during 2020 was based on the results of the analysis of CSSR, CDR, HOSR, TCHCR values. The results showed that the GSM network performance of a BTS at PT. Telecommunications RTPO Berau. The performance results in the first BTS with CSSR values = 99.31%, CDR = 0.30%, HOSR = 94.74%, TCHCR = 0.01%, and for the second BTS performance values CSSR = 98.66%, CDR = 0.35%, HOSR = 96.78%, TCHCR = 0%, and the third BTS with CSSR = 99.74%, CDR = 0.03%, HOSR = 98.01%, TCHCR = 0%. Based on the results of the analysis of the performance of the GSM network in the Berau area and compared with the standard parameters of the KPI (Key Performance Indicator) it can be concluded that the performance of the GSM network in the three BTS is good.
Game Edukasi Berbasis Android sebagai Media Pembelajaran Matematika untuk Anak Tunarungu Rizki Yusliana Bakti; Titin Wahyuni; Muhyiddin A M Hayat; Ridwang Ridwang
PROtek : Jurnal Ilmiah Teknik Elektro Vol 8, No 1 (2021): PROtek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v8i1.2377

Abstract

Education is a right for every individual. Not only those with normal conditions, but also those with special needs such as the deaf. Limited intellectual ability in deaf children has consequences for their difficulties in attending academic lessons including maths lessons. This research aims to create an application as a learning medium to attract deaf children in developing their intelligence. This application was created as a means to provide convenience to deaf children in helping the learning process of mathematics. This study uses observation data collection methods, interviews / questionnaires and library studies. The design method used is the waterfall and the testing technique used is Integration and System testing. The result of this study is an android-based game application named math games. The test results show that this application is easy to learn and there are media that make childrenhappy.
PEMBUATAN VERIFIKASI SERTIFIKAT DIGITAL SEBAGAI BUKTI KEABSAHAN MENGGUNAKAN ALGORITMA STEGANOGRAFI DENGAN METODE LEAST SIGNIFICANT BIT INSERTION (LSB) Muh Nur Aqsal Aminullah; Rizki Yusliana Bakti; Muhyiddin AM Hayat; Lukman Lukman
Ainet : Jurnal Informatika Vol 4, No 1 (2022): Maret (2022)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/ainet.v4i1.11904

Abstract

Tujuan dari penelitian ini adalah untuk memberikan sebuah kemudahan dalam proses pembuatan sertifikat terutama untuk mendapatkan tanda tangan digital menggunakan sistem autentikasi dan memberikan sebuah keamanan pada sertifikat digital menggunakan algoritma steganografi dengan metode least significant bit. Sertifikat digital adalah sebuah sertifikat yang bersifat elektronik dimana memuat sebuah tanda tangan digital penyelenggara kegiatan dan identitas dari pemilik sertifikat digital tersebut. Pada sertifikat digital yang dibuat ini menggunakan algoritma steganografi dengan metode least significant bit sebagai sistem keamanan dari sertifikat digital tersebut. Steganografi ini adalah sebuah teknik penyembunyian pesan rahasia yang hanya dapat diketahui oleh pengirim dan penerima tanpa menimbulkan kecurigaan. Least significant bit ini adalah salah satu metode yang terdapat pada algoritma steganografi dimana proses penyembunyian pesannya adalah dengan menggunakan wadah dengan dengan format image JPG dan menyisipkan pesan rahasia ke dalam bit pixel pada wadah tersebut. Hasil yang didapatkan dari penelitian yang dilakukan menunjukkan bahwa sistem autentikasi yang dibuat untuk mendapatkan tanda tangan digital penyelenggara berhasil dilakukan dengan mengirimkan pesan notifikasi permintaan untuk mendapatkan tanda tangan digital dan keamanan yang diberikan pada sertifikat digital yang berhasil dibuat juga berhasil dipasangkan dan pengecekan terhadap sertifikat digital juga berhasil dilakukan agar dapat mengetahui sertifikat digital tersebut asli atau hasil duplikasi atau modifikasi pihak ketiga.
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.
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.
Co-Authors . Darniati Akbar DB, Andi Muhammad Akbar, Syahril Anas, Andi Lukman Anas, Lukman Anggreani, Desi Bakti, Rizki Yusliana Danuputri, Chyquitha Danuputri, Chyquitha Faeruddin, Muhammad Asygar Hadawina Hadawina Lukman Lukman Lukman Muh Nur Aqsal Aminullah Muhammad Faisal Mujidah, Jihan Izzathul RAHMANIA Rahmania Ridwang Ridwang Risal RIZKI YUSLIANA BAKTI S. Kuba, Muhammad Syafa'at Sarina Setiawan, Dedi Syamsuri, Andi Makbul Syarifuddin, Nur Annisa TITIN WAHYUNI Wahyuni, Titin