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All Journal bit-Tech Jurnal Nasional Ilmu Komputer Informanika Jurnal Komputer, Informasi dan Teknologi
Zaid Romegar Mair
Universitas Indo Global Mandiri Palembang. Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Other

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APLIKASI MEDIA BELAJAR PRAKTEK SHOLAT MENGGUNAKAN TEKNOLOGI AUGMENTED REALITY BERBASIS ANDROID Romegar Mair, Zaid
JURNAL INFORMANIKA Vol 4, No 2 (2018)
Publisher : POLITEKNIK ANIKA

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

Abstract

Sholat merupakan media komunikasi antara manusia dengan Allah SWT. Pendidikan mengenai sholat biasanya diajarkan dengan metode yang konvensional misalnya disekolah dengan bimbingan guru atau dirumah dengan bimbingan orangtua. Seiring dengan berkembangnya pengetahuan dan teknologi yang pesat, maka peran user dalam mendidik anak dalam hal praktek sholat, sangat membantu para orang tua terhadap teknologi yang disimulasikan dengan objek 3 dimensi melalui pengindaian menggunakan kamera smartphone atau tablet. Pada aplikasi praktek sholat yang dibuat dengan teknologi Augmented Reality yang diinstal pada smartphone berbasis android, dimana user bisa membaca dan mendengarkan bacaan sholat. Bacaan sholat tersedia dalam bahasa arab,latin, dan indonesia dan juga terdapat gambar peragaan posisi gerakan sholat. Software yang digunakan dalam aplikasi ini yaitu unity 3D dan adobe photoshop, sedangkan output yang dihasilkan dari penelitian ini berupa aplikasi android dalam bentuk apk, yang dipindahkan pada perangkat android dan instal aplikasi praktek sholat. KataKunci : Sholat, Unity 3D, Smartphone Android, Augmented Reality
Sistem Informasi Pengelolaan Data Alumni Pada UPT SMK Negeri 1 Musi Banyuasin Risviliani Afrilia; Zaid Romegar Mair; Juansyah Juansyah
Jurnal Nasional Ilmu Komputer Vol. 2 No. 2 (2021): Jurnal Nasional Ilmu Komputer
Publisher : Training and Research Institute Jeramba Ilmu Sukses (TRI - JIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jurnalnik.v2i2.527

Abstract

This research was conducted to create an Alumni data management information system at UPT SMK Negeri 1 Musi Banyuasin. This system designed specifically for managing alumni data is expected to make it easier for staff to manage alumni data and make it easier for alumni to fill in alumni data without having to come to school. The focus in this research is to create a system using programming languages ​​that have been widely used, namely PHP and MySQL and to report the data using FPDF. Data Collection Methods in this study are about the Alumni data management information system at UPT SMK Negeri 1 Musi Banyuasin. This study shows the results that to present information on the Alumni of UPT SMK Negeri 1 Musi Banyuasin which includes the presentation of alumni data. As well as other information that is deemed necessary to be presented when needed, such as facilitating the search for old or new alumni. The development of this web-based information system will make it easier to handle changes in data, be it adding, subtracting or searching for data.
Classification Of Eligibility For Assistance Recipients Program Indonesia Pintar Using The Naïve Bayes Method Via Kris Savitri; Herri Setiawan; Zaid Romegar Mair
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3002

Abstract

The manual process of determining student eligibility for the Indonesia Pintar Program (PIP) often results in inefficiencies and inaccuracies. Schools are required to evaluate large volumes of socioeconomic data, and errors in judgment may lead to misallocation, where eligible students are excluded and ineligible students are included. Such inefficiencies highlight the need for objective, data-driven approaches. This study aims to evaluate the performance of the Naïve Bayes classification algorithm in classifying PIP eligibility, with a particular focus on attribute selection and its effect on classification accuracy. Historical student data from a primary school (SDN 1 Sindang Marga), which has rarely been examined in previous works and the analysis of attribute selection strategies, showing that fewer but more relevant attributes can yield better results. A dataset of 172 students was pre-processed and divided into training (80%) and testing (20%) subsets. Model evaluation was conducted using confusion matrices to calculate accuracy, precision, recall, and F1-score. The results demonstrate that using four attributes parental occupation, parental income, KPS ownership, and KIP ownership achieved the highest performance, with 85.3% accuracy, 92.0% precision, 88.5% recall, and a 90.2% F1-score. By contrast, using all seven attributes resulted in slightly lower accuracy (82.4%). These findings highlight that selective attribute use improves model efficiency and accuracy. Beyond methodological contributions, this research provides practical implications by demonstrating how machine learning can enhance fairness, transparency, and objectivity in educational aid distribution.
Sentiment Analysis of Public Reviews on the Internet for Gelora Sriwijaya International Stadium in Palembang Using Random Forest Classifier Title Yoga Seprana; Herri Setiawan; Zaid Romegar Mair
Jurnal Komputer, Informasi dan Teknologi Vol. 5 No. 2 (2025): Desember
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v5i2.3036

Abstract

This study was conducted with the aim of analyzing the sentiments contained in public reviews of the Gelora Sriwijaya International Stadium in Palembang. The analysis utilized the Random Forest Classifier algorithm as the main method for the classification process. The data source was obtained from Google Maps through web scraping using the Instant Data Scraper tool, resulting in a total of 1,000 reviews written between 2019 and 2025. These reviews then went through a series of preprocessing stages, including data cleaning to remove irrelevant information, case folding to standardize letter formats, stemming to return words to their root form, stopword removal to eliminate common words with little semantic value, and tokenization to split the text into individual word units. Subsequently, the text data was transformed into a numerical representation using the Term Frequency–Inverse Document Frequency (TF-IDF) method to make it suitable for processing by the classification algorithm. Sentiment classification was carried out by categorizing reviews into two main classes: positive sentiment and negative sentiment. The evaluation results showed that the model achieved an accuracy of 89% when tested using the K-Fold Cross Validation technique, which was higher than the 84% accuracy obtained without validation. These findings demonstrate that the Random Forest Classifier algorithm is highly capable of performing sentiment analysis and has the potential to serve as a useful tool for stadium management to understand public perceptions and improve the quality of facility management.
Co-Authors Herri Setiawan Juansyah Juansyah Risviliani Afrilia Via Kris Savitri Yoga Seprana