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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Information Systems and Informatics Ainet : Jurnal Informatika International Journal of Industrial Optimization (IJIO) Mattawang: Jurnal Pengabdian Masyarakat Jurnal Indonesia : Manajemen Informatika dan Komunikasi Nusantara Hasana Journal Journal of Computer and Information System (J-CIS) Indonesian Community Journal Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Progres Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal of Computer Science and Information Technology Journal of Muhammadiyah’s Application Technology
Ida Mulyadi, Ida
Muhammadiyah University Of Makassar
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Microsoft Copilot Training for Monitoring Student Learning: A Case Study Vocational High School Makassar - Indonesia Dikwan Moeis; Nasir Usman; Muhammad Faisal; Andi Harmin; Ida Mulyadi; Musdalifa Thamrin
I-Com: Indonesian Community Journal Vol 4 No 3 (2024): I-Com: Indonesian Community Journal (September 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v4i3.5134

Abstract

Artificial intelligence (AI) has become an increasingly popular technology and brings significant educational benefits. This technology increases the learning process's efficiency and productivity, allowing for the development of students' abilities in a more focused manner. AI is a catalyst in preparing generations to face future challenges. One example of AI's application in education is Microsoft Copilot, an artificial intelligence model developed by Microsoft in collaboration with OpenAI. Microsoft Copilot is designed to understand and support various academic tasks through human-like interactions. Training on using Microsoft Copilot was carried out for students of SMKS Wahyu Makassar. This training aims to support the learning process, increase learning effectiveness, and assist students in doing academic assignments. The evaluation results showed that Microsoft Copilot provided significant benefits, with positive feedback from participants. Most students found this training useful, easy to understand and improved their knowledge.
Machine learning for global trade analysis: a hybrid clustering approach using DBSCAN, elbow, and SOM Thamrin, Musdalifa; Mulyadi, Ida; Made Widia, I Dewa; Faisal, Muhammad; Hi Baharuddin, Suardi; Prihatmono, Medy Wismu; Nurdiansyah, Nurdiansyah; Usman, Nasir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3033-3046

Abstract

Global trade constitutes a highly complex and interdependent system influenced by diverse economic, geographic, and political factors. This study proposes a hybrid clustering framework that integrates density-based spatial clustering of applications with noise (DBSCAN), elbow, and self-organizing maps (SOM) methods to uncover latent structures in international trade patterns. Utilizing averaged trade data from 25 countries spanning the period from 2013 to 2023, the framework identifies distinct clusters based on export-import characteristics. The DBSCAN is employed to detect dense trade hubs and outlier behaviors, the elbow method determines the optimal number of clusters, and SOM facilitates the visualization of non-linear, high-dimensional trade relationships. The analysis reveals three prominent trade clusters: Global Trade Leaders, Emerging Trade Powers, and Niche Exporters, each reflecting varying degrees of trade diversification and dependency. These empirical findings align with established economic theories, including the Heckscher Ohlin model and dependency theory, and provide actionable insights for policymakers seeking to enhance trade competitiveness and regional integration strategies.
PENERAPAN ALGORITMA K-MEANS TERHADAP EVALUASI WEBSITE E-COMMERCE Febriyanto A.; Dzulqornain Sabri S. Anggie; Mulyadi, Ida
Nusantara Hasana Journal Vol. 3 No. 12 (2024): Nusantara Hasana Journal, May 2024
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v3i12.1124

Abstract

Facing large amounts of high-dimensional transaction data, clustering approaches often face challenges that include elasticity, weak high-dimensional data processing capabilities, sensitivity to data order over time, independence from parameters, and the ability to manage noise. These problems can limit a method from producing accurate predictions. Experiments conducted with data samples collected from 50 different mobile phones purchased on Lazada yielded the following results: K-means outperforms Single-pass in evaluating e-commerce transactions because it has higher intra-class dissimilarity and inter-class similarity. K-means clustering is an approach to the effective and flexible organization of large datasets. The results of a clustering algorithm are sensitive not only to the total number of clusters but also to how they were originally arranged. Therefore, it is easy to show that the clustering results are locally optimized. Further research conducted into the elements that influence the number of clusters produced by this method as well as the initial location of clustering centers is a very important endeavor.
ANALYSIS OF DETERMINING PERMANENT EMPLOYEES USING OCRA (OPERATIONAL COMPETITIVENESS RATING ANALYSIS) METHODOLOGY Darniati; Nurahmad, Nurahmad; Mulyadi, Ida Mulyadi; Musdalifa Thamrin; Samsuria, Samsuria; Muhammad Faisal
Nusantara Hasana Journal Vol. 4 No. 1 (2024): Nusantara Hasana Journal, Juny 2024
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v4i1.1143

Abstract

The determination of permanent employees is a crucial strategic decision for every organization. This decision affects productivity, efficiency, and work culture within the company. This study aims to analyze and determine permanent employees using the OCRA (Operational Competitiveness Rating Analysis) Method. OCRA is a method that assesses employee performance based on indicators of Length of Work, Work Loyalty, Age, Last Education, Work Performance, Communication Skills, and Teamwork Skills that are relevant to the operations of a company. In this study, employee performance data is collected and analyzed using a quantitative approach. Each employee is assessed based on a number of criteria that include productivity, quality of work, compliance with procedures, and contribution to the team and the company as a whole. The results of the OCRA analysis are then used to identify employees who have superior performance and deserve to be considered as permanent employees. The results of the study show that the OCRA Method is able to provide an objective and measurable assessment in determining permanent employees. A3-employees with the highest OCRA scores are proven to have consistent performance and a significant contribution to the achievement of company goals. This study concludes that the use of the OCRA Method in determining permanent employees can increase transparency and accuracy in human resource management decision-making.
Penerapan Metode Best First Search pada Sistem Informasi Penjualan Online Mulyadi, Ida
Journal of Computer and Information System ( J-CIS ) Vol 4 No 2 (2021): J-CIS Vol 4 No. 2 Tahun 2021
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jcis.v4i2.1203

Abstract

Kasih dan Sayang merupakan produsen kue cokelat makalate yang berpusat di Makassar. Perusahaan tidak pernah mengukur sejauh mana kegiatan pemasarannya berdampak pada penjualan dan dianggap tidak efektif untuk menarik konsumen, karena belum memanfaatkan teknologi didalam memasarkan atau menginformasikan hasil produksinya ke msayarakat. Tujuan dari penelitian adalah untuk membantu perusahaan dalam memasarkan produk kue coklat dengan pemanfaatan aplikasi sistem informasi penjualan, sehingga dapat menarik minat konsumen dalam pembelian berbagai macam jenis kue coklat yang ditawarkan. Dalam penelitian ini menggunakan metode Best First Search yang merupakan pencarian Heuriristic sebagai pencarian kata pada sistem informasi penjualan. Hasil dari penelitian ini berbentuk website yang dibangun dan dirancang menggunakan bahasa pemrograman PHP. Pengujian kualitas sistem ini menggunakan metode System Usability Scale dari para pengguna dengan perolehan nilai 72,75 dengan grade C berstatus memuaskan.
Utilization of Artificial Intelligence to Support Technology Development at PT. Aplikanusa Lintasarta – Makassar Muhammad Faisal; Nasir Usman; Ida Mulyadi; Rosnani Rosnani; Darniati Darniati; Musdalifa Thamrin; Mardiah Mardiah; Alvina Felicia Watratan
I-Com: Indonesian Community Journal Vol 5 No 2 (2025): I-Com: Indonesian Community Journal (Juni 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/icom.v5i2.6945

Abstract

This community service activity aimed to enhance the understanding of Machine Learning (ML) and Deep Learning (DL) technologies among employees of PT. Aplikanusa Lintasarta, as an academic contribution to supporting the company’s digital transformation acceleration. Conducted in a hybrid format (offline and online) on April 21, 2025, the program featured expert speakers and employed an interactive outreach approach combined with applicable case studies. To assess its effectiveness, pre-test and post-test instruments were utilized, revealing an average increase of 45% in participants’ comprehension. Participants' responses were highly positive, as demonstrated by their enthusiasm during discussions and interest in implementing ML/DL within the workplace. This activity not only strengthened internal technological literacy but also supported the development of the national AI ecosystem, in alignment with the launch of GPU Merdeka by Lintasarta.
Penerapan Sistem Pencarian Dokumen Berdasarkan Frasa di Abstrak Perpustakaan Digital Menggunakan Algoritma BM25 dan Word2Vec Fahrim Irhmna Rachman; Ida Mulyadi; Fajar, Nur
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/t9pgjs86

Abstract

Perkembangan perpustakaan digital menyebabkan meningkatnya volume abstrak dokumen sehingga menuntut metode pencarian yang akurat untuk menemukan buku relevan. Penelitian ini mengusulkan penerapan sistem pencarian berbasis frasa pada abstrak dengan menggabungkan algoritma BM25 dan Word2Vec untuk meningkatkan relevansi hasil. Dataset terdiri dari 500 abstrak skripsi yang dipreproses (lowercasing, tokenisasi, stopword removal); model Word2Vec dilatih dengan arsitektur skip-gram (vector_size=100, window=5, epochs=50) dan BM25 diinisialisasi pada representasi token dokumen. Skor BM25, Word2Vec (cosine similarity) dan TF-IDF dinormalisasi lalu digabungkan (rata-rata) untuk pemeringkatan akhir. Evaluasi dilakukan menggunakan metrik Precision, Recall dan F1-Score pada beberapa query uji. Hasil menunjukkan peningkatan performa pada banyak query (rata-rata F1 ≈ 0.80) dengan beberapa kasus mencapai nilai sempurna (1.00), meskipun ada variabilitas antar tipe query. Temuan ini menegaskan bahwa penggabungan pencocokan lesikal BM25 dan representasi semantik Word2Vec dapat meningkatkan relevansi pencarian; pengembangan lanjutan direkomendasikan pada metode penggabungan skor dan perluasan korpus.
Enhancing YOLOv12-Based Rice Leaf Disease Detection through Evaluation of Three Data-Split Scenarios Ida Mulyadi; Fahrim Irhamna; Chyquitha Danuputri; Ridwang; Ridha Awalia
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1580

Abstract

One of the most significant staple crops in the world is rice, and one of the main causes of the drop in agricultural yields is illnesses that affect rice leaves. To avoid large agricultural losses, early diagnosis of these illnesses is essential. The goal of this project is to use YOLOv12, the most recent deep learning-based object detection architecture, to create a rice leaf disease detection system. The model was trained using a dataset of 4,744 photos of rice leaves that included three disease classes: Leaf Blast, Brown Spot, and Bacterial Leaf Blight. Methods to boost variability and enhance detection performance, image preprocessing with data augmentation was used. Standard object detection criteria, such as mean Average Precision (mAP), precision, and recall, were used to assess the model. The YOLOv12 model was highly effective in detecting rice leaf illnesses. According to the experimental data, it achieved a mAP of 97%, a precision of 96%, and a recall of 96.5%. The use of YOLOv12's greater efficiency and quality in detecting small objects—which is essential for identifying illness symptoms on leaves—is what makes this study successful. These results lay the groundwork for upcoming precision agricultural real-time monitoring applications.
SISTEM KELAYAKAN PENERIMA BANTUAN SOSIAL MENGGUNAKAN ALGORITMA CATBOOST CLASSIFIER (STUDI KASUS KABUPATEN LUWU) Besse Taleha; Ida Mulyadi; Fahrim Irhamna Rachman
Journal of Computer Science and Information Technology Vol. 3 No. 3 (2026): Juni
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jcsit.v3i3.3988

Abstract

Penelitian ini membahas pengembangan sistem kelayakan penerima bantuan sosial berbasis situs web dengan menerapkan algoritma CatBoost Classifier pada studi kasus Kabupaten Luwu. Dataset awal yang digunakan berjumlah 1.670 data calon penerima bantuan sosial. Permasalahan utama penelitian adalah proses seleksi penerima bantuan yang masih berpotensi mengalami ketidaktepatan sasaran karena melibatkan banyak variabel sosial ekonomi. Penelitian ini bertujuan membangun sistem yang mampu mengelola data masyarakat dan memberikan klasifikasi status Layak atau Tidak Layak. Variabel yang digunakan meliputi usia, pekerjaan, penghasilan per bulan, jumlah tanggungan, kondisi rumah, aset, dan status kelayakan sebagai label. Tahapan penelitian meliputi pengumpulan data, prapengolahan, pembagian data, pelatihan model CatBoost, implementasi sistem, serta pengujian Black Box. Hasil evaluasi menunjukkan akurasi 93,71% pada data uji dengan precision kelas Layak sebesar 0,94, recall kelas Layak sebesar 0,92, precision kelas Tidak Layak sebesar 0,94, dan recall kelas Tidak Layak sebesar 0,95. Sistem yang dibangun mampu membantu proses pendataan dan rekomendasi kelayakan bantuan sosial secara lebih terstruktur.
PENERAPAN EXTREME GRADIENT BOOSTING (XGBOOST) PADA DIAGNOSA PENYAKIT MATA Ilfauza Febrianty Faisal; Chyquithadanu putri; Ida Mulyadi
Journal of Computer Science and Information Technology Vol. 3 No. 3 (2026): Juni
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jcsit.v3i3.3989

Abstract

Penelitian ini bertujuan menerapkan algoritma Extreme Gradient Boosting (XGBoost) untuk mengklasifikasikan penyakit mata berbasis data gejala pasien, khususnya pada tiga kelas penyakit, yaitu katarak, glaukoma, dan retinopati diabetik. Metode yang digunakan adalah kuantitatif dengan pendekatan machine learning. Data penelitian berasal dari RSUD Batara Guru dan terdiri atas 1.041 data pasien setelah melalui tahap preprocessing. Sebanyak 15 fitur gejala diekstrak dan digunakan sebagai variabel input, meliputi kabur, mata merah, sensitif cahaya, sakit kepala, gatal, nyeri, mata berair, kelopak mata bengkak, sulit buka mata, warna pudar, sulit melihat malam, floaters, pupil abnormal, mata berat, dan mual. Tahapan penelitian mencakup pemeriksaan data, transformasi nilai gejala, encoding label diagnosa, pemilihan fitur, pembagian data training dan testing, pelatihan model XGBoost, serta evaluasi menggunakan confusion matrix. Model menghasilkan performa seimbang di atas 93,7% pada seluruh metrik evaluasi, yaitu accuracy 93,78%, precision 93,80%, recall 93,78%, dan F1-score 93,75%. Hasil confusion matrix menunjukkan bahwa sebagian besar data berhasil diklasifikasikan dengan benar, meskipun masih terdapat kesalahan pada kelas glaukoma karena kemiripan gejala dengan katarak. Dengan demikian, XGBoost berpotensi digunakan sebagai sistem pendukung keputusan klinis untuk identifikasi awal penyakit mata berbasis gejala pasien, namun hasil prediksi tetap memerlukan konfirmasi medis oleh tenaga kesehatan.
Co-Authors . Darniati Ahmad Fathir Alvina Felicia Watratan Andi Harmin Andi Makbul Syamsuri Ashabul Kahfi Baharuddin, Suardi Hi Besse Taleha Chyquita Danuputri Chyquitha Danuputri Chyquithadanu putri Darniati Darniati Darniati Desi Anggreani Dzulqornain Sabri S. Anggie Fachrim Irhamna Rachman Fachrim Irhamna Rachman Fahrim Irhamna Rachman Fahrim Irhmna Rachman Febriyanto A. Hafipa Sudiadarma Hi Baharuddin, Suardi Ilfauza Febrianty Faisal Indra Aditya Irmawati Irmawati Made Widia, I Dewa Mardiah Mardiah Mardiah Mardiah Medy Wisnu Prihatmono Muh Khayyir Muh. Darmawan Aryadinata Muhammad Faisal Muhammad Syafaat S. Kuba Musdalifa Thamrin Musdalifa Thamrin Nasir Usman Nasir Usman Nur Fajar Nur Ramadhan Nur Ramadhan, Nur Nurahmad Nurdiansyah Nurdiansyah Nurul Qalbi Ridha Awalia Ridwang Rizki Yusliana Rosnani Rosnani Samsuria, Samsuria Selfira Madoa Sry Yunarti Suardi Hi Baharuddin Titik Khawa Abdul Rahman Titin Wahyuni