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All Journal Insect (Informatics and Security) : Jurnal Teknik Informatika International Journal of Industrial Optimization (IJIO) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Nusantara Hasana Journal Indonesian Community Journal Jurnal Informatika Progres
Darniati Darniati
Hasanuddin University, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Nursing Public Health Social Sciences Other

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IMPLEMENTASI ALGORITMA K-MEANS DALAM PENGELOMPOKKAN PEMBERIAN ZAKAT PADA BAZ AL-MARKAZ MAKASSAR SULAWESI SELATAN Darniati
PROGRESS Vol 9 No 1 (2017): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (173.384 KB) | DOI: 10.56708/progres.v9i1.71

Abstract

Problematika utama yang sedang dihadapi oleh Badan Amil Zakat adalah masalah penyaluran dana zakat yang kurang tepat sasaran sesuai dengan syariat Islam. Tulisan ini merupakan hasil penelitian yang bertujuan untuk membantu badan amil zakat dalam menyalurkan dana zakat yang terkumpul dari para musakki (pelaku zakat) kepada para mustahik (penerima zakat) yang sesuai dengan syariat Islam. Pada penelitian ini dibuat suatu sistem yang mengimplementasikan metode K-Means. Metode K-Means ini akan mengolah atribut-atribut yang mempengaruhi proses Klasterisasi penduduk calon penerima dana zakat. Atribut-atribut tersebut terdiri dari penghasilan, pekerjaan, alamat, kepemilikan aset, pendidikan terakhir. Proses Klasterisasi terdiri dari data latih yang akan menjadi parameter pada proses klasterisasi data uji pada sistem. Sistem mengelompokkan apakah seseorang memenuhi syarat untuk menerima zakat atau tidak memenuhi syarat menerima zakat. Hasil penelitian ini akan diimplementasikan kepada Badan Amil Zakat dalam menentukan kelayakan penerima zakat.
Implementation of Master Replication Learning Media and Slave Using Kine Master on Makassar Professional STMIK Satriawaty Mallu; Darniati Darniati
Insect (Informatics and Security): Jurnal Teknik Informatika Vol. 8 No. 1 (2022): Oktober 2022
Publisher : Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/insect.v8i1.1970

Abstract

Proses pembelajaran membutuhkan media yang interaktif dan menarik, masa peralihan dari pandemi ke andemi membuat proses pembelajaran saat ini mengharuskan embedded, online diselingi dengan proses offline. Media pembelajaran membuat Replikasi pada Master dan Slave merupakan bagian dari Mata Kuliah Pengolahan Data Terdstribusi pada STMIK Profesional Makassar yang bertujuan untuk mengurangi tingkat kejenuhan mahasiswa belajar online. Aplikasi Kine Master memudahkan mahasiswa untuk memahami proses pembelajaran karena materi yang dikelola dalam bentuk animasi tampilan dalam bentuk video dan teks. Replikasi salah satu metode penyimpanan database pada basisdata terdistribusi. Mahasiswa mampu membuat Replikasi Master dan Slave, mengetahui sejauh mana Efektifitas penggunaan Media Pembelajaran Aplikasi Kine Mater untuk Membuat Replikasi Master dan Slave.
IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR TERHADAP PENENTUAN RISIKO KREDIT USAHA MIKRO KECIL DAN MENENGAH Ida; Suardi Hi Baharuddin; Muhammad Faisal; Nur Ramadhan; Darniati
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.163

Abstract

This research was carried out in the context of implementing the K-NN algorithm so that a source of information can be produced as a basis for supporting decisions on initial credit applications by customers so that they can help cooperative managers more as knowledge of the progress of credit proposals that are carried out at the Micro, Small and Medium Enterprise Cooperative Service Office ( SMEs) South Sulawesi Province. The K-Nearest Neighbor algorithm is used to classify objects based on attributes and training samples. Among them, from k objects, the k-Nearest Neighbor algorithm uses neighbor classification as the predicted value. The results show that the algorithm produces a classification with a faster calculation time based on the prediction of customer data resulting from the calculation.
Embedded response technology and service cloud platform for vehicle information tracking Muhammad Faisal; Ida Ida; Darniati Darniati; Irmawati Irmawati; Muh Khayyir
International Journal of Industrial Optimization Vol. 4, No. 1 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v4i1.6928

Abstract

Based on the Indonesia national police crime database, it is reported that vehicle theft cases have increased during the Covid-19 pandemic. The database reported an increasing trend of vehicle theft, 4,065 cases from January 2019 to January 2020 in the province and regency region. Therefore, to help police officers work and minimize the criminal cases of vehicle theft, an effective strategy is needed to reduce these threats. This study proposes implementing SMS and QRcode technology embedded in the vehicle for validation information. Cloud computing capabilities can offer real-time network access to technology resources that can be physically located anywhere geographically based on business needs. This technology can rapidly search and show detailed information regarding the specific vehicle, including the vehicle owner, the vehicle registration number, and the validation of the driver's license. To implement and examine the effectiveness of the proposed technology, this study was conducted an experimental study in a real-world setting from January 2021 until April 2021 in Makassar city, Indonesia. This study concluded that the proposed technology could successfully be implemented and effectively show detailed information regarding the specific vehicle based on the experimental results. This study concluded the potential use of the proposed technology in the real world as an alternative solution to minimize the criminal cases of vehicle theft. It can be used as an alternative solution to reduce the increase in criminal cases of inter-island private vehicle theft syndicates.
COMPARISON OF THE PERFORMANCE OF REGRESSION-SPECIFIC AND MULTI-PURPOSE ALGORITHMS Usman, Nasir; Darniati, Darniati; Rosnani, Rosnani; Musdalifa Thamrin; Nurahmad, Nurahmad; Nurdiansyah, Nurdiansyah; Faisal, Muhammad
Nusantara Hasana Journal Vol. 4 No. 8 (2025): Nusantara Hasana Journal, January 2025
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v4i8.1274

Abstract

Regression is a data science method for evaluating the relationship between independent and dependent variables. This study compares the performance of various regression algorithms using the Boston Housing Dataset, which consists of 506 samples divided into 80% for training and 20% for testing. Performance evaluation was conducted using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). All algorithms were implemented with default hyperparameter settings provided by the Scikit-learn library to ensure fair comparison. The results showed that versatile algorithms, particularly Gradient Boosting Machines (GBM) and Random Forest, achieved the best performance with R² values of 0.92 and 0.89, respectively, and lower errors. Conversely, regression-specific algorithms, such as Linear Regression and Ridge Regression, recorded R² values of approximately 0.67, while the k-Nearest Neighbors algorithm had the lowest performance with an R² of 0.65. Versatile algorithms proved to be more effective for datasets with complex non-linear patterns, while regression-specific algorithms were better suited for linear data patterns. These findings provide guidance for practitioners in selecting algorithms based on data characteristics and analysis objectives.
Utilization of Artificial Intelligence to Support Technology Development at PT. Aplikanusa Lintasarta – Makassar Faisal, Muhammad; Usman, Nasir; Mulyadi, Ida; Rosnani, Rosnani; Darniati, Darniati; Thamrin, Musdalifa; Mardiah, Mardiah; Watratan, Alvina Felicia
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.
Co-Authors Alvina Felicia Watratan Ida Mulyadi Irmawati Irmawati Mallu, Satriawaty Muh Khayyir Muhammad Faisal Mulyadi, Ida Musdalifa Thamrin Nasir Usman Nur Ramadhan Nurdiansyah Nurdiansyah Rosnani Rosnani Rosnani Rosnani Suardi Hi Baharuddin