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All Journal PROtek : Jurnal Ilmiah Teknik Elektro Jurnal INSYPRO (Information System and Processing) JTERA (Jurnal Teknologi Rekayasa) JURNAL INSTEK (Informatika Sains dan Teknologi) Ainet : Jurnal Informatika Jurnal Digital Teknologi Informasi Madaniya Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Informatika Progres ARUS JURNAL SAINS DAN TEKNOLOGI Journal of Muhammadiyah’s Application Technology
Bakti, Rizki Yusliana
Informatika, Universitas Muhammadiyah Makassar
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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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.
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.
A Bluetooth-Based Attendance System for Educational Administration at SMA Muhammadiyah: Cross-Platform Development and Usability Validation Hayat, Muhyddin A.M.; Rasyidi, Muhammad Fachri; Faisal, Muhammad; Bakti, Rizki Yusliana; Syamsuri, Andi Makbul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4873

Abstract

The transformation of educational administration through technology has accelerated significantly, particularly in attendance systems, which have traditionally relied on manual roll calls. These conventional methods are time-consuming, error-prone, and susceptible to manipulation. This study presents a novel Bluetooth-based attendance system that contributes to the field by demonstrating passive MAC address detection for automated attendance recording, eliminating the need for additional software installations on student devices. The system was developed using React Native for cross-platform compatibility, with PostgreSQL for data management and NestJS for backend processing. The software engineering process followed Rapid Application Development (RAD) methodology, combined with comprehensive system validation through experimental testing. Usability evaluation with 133 participants using the System Usability Scale (SUS) yielded a score of 79.85, categorizing the system within the "Good to Excellent" usability range. The findings demonstrate significant improvements in efficiency and a reduction in attendance fraud compared to conventional methods. However, hardware quality and device proximity remain key limitations. Future research should explore the integration of Bluetooth Low Energy (BLE) technology, the implementation of machine learning algorithms for anomaly detection, or the development of hybrid validation models that combine multiple authentication factors. This system demonstrates the potential to modernize educational administration through seamless, device-level integration while maintaining high user acceptance.
Game Edukasi Berbasis Android sebagai Media Pembelajaran Matematika untuk Anak Tunarungu Bakti, Rizki Yusliana; Wahyuni, Titin; Hayat, Muhyiddin A M; 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.
PENERAPAN ALGORITMA COSINE SIMILARITY DALAM EFEKTIFITAS PENGACAKAN SOAL UJIAN ONLINE Prima Abdiguna, Aidhil; Lukman; Yusliana Bakti, Rizki
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 10 No 2 (2025): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v10i2.56473

Abstract

Pengacakan soal ujian online yang efektif merupakan tantangan penting dalam memastikan keadilan dan keakuratan dalam distribusi soal. Penelitian ini bertujuan untuk mengetahui bagaimana algoritma Cosine Similarity dapat diterapkan dalam sistem pengacakan soal ujian online serta mengevaluasi efektifitasnya dalam pendistribusian soal. Metode Term Frequency-Inverse Document Frequency (TF-IDF) untuk merepresentasikan soal dalam bentuk vektor numerik sebelum dilakukan perhitungan nilai kesamaan oleh algoritma Cosine Similarity, serta metode Mean Absolute Error (MAE) dan Root Mean Squared Error (RMSE) untuk memvalidasi efektifitas hasil pengacakan. Hasil serta kesimpulan dari penelitian menunjukkan bahwa penerapan algoritma Cosine Similarity dalam sistem pengacakan soal dapat dilakukan dengan sebelumnya menerapkan tahap preprocessing data dan Term Frequency-Inverse Document Frequency serta hanya digunakan sebelum tahap pengacakan, dan efektifitas penggunaan algoritma ini dinilai efektif dikarenakan selisih rata-rata antara hasil sistem dan ideal berada dikisaran 0-1, dimana berdasarkan validasi Mean Absolute Error (MAE) sebesar 0,2514 serta Root Mean Squared Error (RMSE) sebesar 0,4704, yang menunjukkan tingkat efektivitas tinggi dalam proses pengacakan.
Model Deep Learning Berbasis Convolutional Neural Network Untuk Identifikasi Stroke Iskemik Pada Citra CT Scan Faturohman, Agung; Anggreani, Desi; Yusliana Bakt, Rizki
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1150

Abstract

Stroke iskemik merupakan salah satu penyakit tidak menular yang berbahaya dan dapat menyebabkan kecacatan hingga kematian apabila tidak ditangani dengan cepat dan tepat. Identifikasi stroke melalui citra CT scan otak menjadi metode penting dalam dunia medis, namun masih memerlukan waktu dan keahlian tinggi. Penelitian ini bertujuan untuk mengembangkan sistem deteksi stroke iskemik secara otomatis menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2. Data yang digunakan berupa citra CT scan otak pasien dari Rumah Sakit Labuang Baji Makassar, yang diproses melalui tahapan preprocessing seperti grayscale, resizing, augmentasi, dan normalisasi. Model CNN dilatih menggunakan binary crossentropy loss dan Adam optimizer untuk klasifikasi dua kelas, yaitu normal dan stroke iskemik. Hasil pengujian menunjukkan bahwa model mencapai akurasi sebesar 91,6%, precision 88%, recall 95,1%, dan F1-score 0,914, yang menandakan bahwa model ini mampu mengenali stroke iskemik secara efektif. Dengan demikian, sistem ini berpotensi menjadi alat bantu diagnosis awal yang efisien dan akurat dalam bidang kesehatan.
Pengenalan Bahasa Isyarat Menggunakan Deteksi Objek Deep Learning Virgiawan, David Arian; Fachrim Irhamna Rahman; Rizki Yusliana Bakti
Ainet : Jurnal Informatika Vol. 7 No. 1 (2025): Maret (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/qkztgb55

Abstract

Berdasarkan perkembangan teknologi, khususnya di bidang komputasi, semakin memungkinkan pengembangan sistem yang mampu mendeteksi bahasa isyarat dengan lebih efisien. Salah satu masalah utama yang dihadapi adalah bagaimana cara mendeteksi dan mengklasifikasi gerakan bahasa isyarat secara akurat menggunakan algoritma YOLOv8. Penelitian ini bertujuan untuk mengimplementasikan YOLOv8 dalam mendeteksi dan mengklasifikasi abjad pada bahasa isyarat Indonesia (SIBI). Penelitian ini dilakukan di Universitas [Nama Universitas], dengan menggunakan dataset yang dikumpulkan melalui pengambilan foto simbol tangan abjad A-Z yang kemudian diproses untuk pelabelan dan pelatihan model. Proses pelatihan model dilakukan menggunakan data yang dibagi menjadi tiga bagian: pelatihan (60%), validasi (20%), dan pengujian (20%). Pengujian model menghasilkan tingkat akurasi yang sangat tinggi sebesar 99,5%, dengan presisi 99,1%, dan recall 99,4%. Hasil ini menunjukkan bahwa sistem yang dikembangkan sangat andal dalam mendeteksi bahasa isyarat secara real-time. Penelitian ini menyarankan agar penelitian selanjutnya menambahkan variasi data isyarat dari berbagai pengguna untuk memperkaya dataset, serta mempertimbangkan penggunaan algoritma terbaru atau penggabungan beberapa algoritma untuk meningkatkan kinerja deteksi .Kata Kunci : Pengenalan Bahasa Isyarat, YOLOv8, Deep Learning, Deteksi Objek, SIBI
Analisis Data Kepuasan Mahasiswa Terhadap Sarana Dan Prasarana Dengan Menggunakan Algoritma Naïve Bayes Pada Universitas Muhammadiyah Makassar suriani, suriani; Muhyiddin A.M Hayat; Rizki Yusliana Bakti
Ainet : Jurnal Informatika Vol. 7 No. 2 (2025): September (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/wrccj205

Abstract

SURIANI, Salah satu institusi pendidikan tinggi yang berperan sentral dalam menyediakan pendidikan berkualitas adalah Universitas Muhammadiyah Makassar (Unismuh). Perguruan tinggi sebagai penyedia layanan harus menyediakan mutu pelayanan yang unggul kepada mahasiswa. Salah satu atribut pelayanan adalah hal yang tidak berwujud. Dalam konteks mahasiswa terhadap pelayanan fasilitas, pegawai dan kepuasan staf pengajar perlu memberikan pelayanan berkualitas tinggi kepada mahasiswa sesuai dengan kebutuhan mereka. Mahasiswa telah mengorbankan uang dan waktu untuk pendidikan, sehingga perguruan tinggi harus memberikan layanan yang sebanding dengan pengorbanan ini. Dari perhitungan yang telah dilakukan dengan metode Naïve Bayes, terdapat terdeteksi terdapat 16 mahasiswa mahasiswa yang menyatakan puas dan 427 mahasiswa yang menyatakan tidak puas, dengan perbandingan persentase 4% banding 96%. Dengan nilai presisi, recall, dan f1 score masing-masing bernilai 100%. Artinya pihak kampus perlu melakukan pengembangan dan perbaikan terhadap sarana dan prasarana yang ada pada kampus universitas Muhammadiyah makassar.
Co-Authors A M Hayat, Muhyddin Adrianingsih, Rizka Ahmad Risal Akbar, Syahril ANDI AGUNG DWI ARYA BULU Andi Yusri Anggreani, Desi Aritmawijaya, Suandi Chatarina Umbul Wahyuni Dewi MJ, Wanda Tyrana Dewi, Syamrilla Fachrim Irhamna Rachman Faeruddin, Muhammad Asygar Fahrim Irhmna Rachman Faturohman, Agung Hadawina Hadawina Haruna, Hanjas Hayat, Muhyddin A.M Indriani, Lis Iskandar, Aryansyah Ismail, La Ode Taufik Kamsurya, Rianita Kazman Riyadi La Ode Taufik Ismail La Ode Taufik Ismail Lukman LUKMAN ANAS Lukman Lukman Lukman Lukman LUKMAN, LUKMAN Maharani, Afifah Muh Nur Aqsal Aminullah Muhammad Faisal Muhyiddin A.M Hayat Mujidah, Jihan Izzathul Muslimah, Nurul Aulia Nandy Rizaldy Najib Nur Alam Nur, Makmur Jaya Prima Abdiguna, Aidhil Rahman, Fahrim Irhamna RAHMANIA Rahmania Rasyidi, Muhammad Fachri Reski Abbas Reski Awalia Ridwang Ridwang Ridwang Ridwang Ridwang, Ridwang S. Kuba, Muhammad Syafa'at Salam, Abd Sarina Sri Hastati suandi_17, suandi_aritmawijaya Sulaeman Suriani Suriani Syamsuri, Andi Makbul TANTRI INDRABULAN Thariq, Ahmad Titin Wahyuni Usman, Ansharullah Patiroi Utama, Prengki Putra Virgiawan, David Arian Wibawa. Ar, Arya Yanti, Wilda Yumi