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All Journal JUSIFO : Jurnal Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Sistemasi: Jurnal Sistem Informasi Briliant: Jurnal Riset dan Konseptual Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Sistem Cerdas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Indonesian Journal of Informatic Research and Software Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science CONSEN: Indonesian Journal of Community Services and Engagement METRIK SERIAL TEKNOLOGI DAN SAINS
Zarnelly Zarnelly
Jurusan Sistem Informasi Universitas Islam Negeri Sultan Syarif Kasim Riau
Religion Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Physics Social Sciences Other

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Journal : building of informatics technology and science

 1 
Klasifikasi Penerima Bantuan Beras Miskin Menggunakan Algoritma K-NN, NBC dan C4.5 Pristiawati, Andani Putri; Permana, Inggih; Zarnelly, Zarnelly; Muttakin, Fitriani
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3617

Abstract

One of the tasks of the Dumai City Social Service is to provide poor rice assistance to people in need. The problem that often occurs in the distribution of rice to the poor is that the target recipients of poor rice often occur. In overcoming the existing problems, this research has carried out classification models using the K-Nearest Neighbor (K-NN) algorithm, Naïve Bayes Classifier (NBC), and C4.5 Algorithm. Based on the experimental results, it was found that the best classification model was produced by the K-NN Algorithm with a value of K equal to 21. Besides that, the C4.5 algorithm succeeded in making a decision tree for the classification model with the lowest complexity because it succeeded in reducing the number of attributes from 33 to 5 attributes. The decision tree can be used as material for consideration to the Social Service in making decisions on Raskin beneficiaries.
Klasifikasi Penerima Bantuan Program Indonesia Pintar (PIP) Pada Siswa SMK Menggunakan Algoritma KNN, NBC dan C4.5 Putra, Tandra Adiyatma; Permana, Inggih; Zarnelly, Zarnelly; Megawati, Megawati
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6395

Abstract

The Indonesia Smart Program (PIP) is a government initiative aimed at providing educational assistance to students from underprivileged families. This research was conducted at SMKN 4 Pekanbaru to enhance the accuracy of distributing PIP aid using data mining methods. Three classification algorithms were used to identify students eligible for assistance: K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), and C4.5. The data used in this study included attributes such as parental occupation, income, and the type of transportation used. The data processing involved cleaning, normalization, and splitting into test and training sets. The results showed that the KNN algorithm performed best with an accuracy of 84.20%, precision of 89.83%, and recall of 99.18%. The C4.5 algorithm excelled in model simplicity, while NBC showed less optimal results compared to KNN.
Penerapan Support Vector Machine untuk Analisis Sentimen Pengguna X terhadap IndiHome, Biznet, dan Starlink Alfian, Zhevin; Afdal, M; Novita, Rice; Zarnelly, Zarnelly
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7429

Abstract

This study aims to analyze user sentiment on the social media platform X toward three major internet service providers in Indonesia, IndiHome, Biznet, and Starlink. The analysis focuses on five key variables: internet speed, network stability, pricing and service packages, customer service quality, and coverage availability. A total of 4,500 data points were collected through data crawling, then processed using text mining techniques and the Support Vector Machine (SVM) algorithm, with data imbalance addressed through the Random Oversampling method. Evaluation results show that IndiHome consistently demonstrated the best performance, achieving an accuracy of up to 90% in the customer service quality variable, and an overall average accuracy above 85% across all variables. Biznet generally ranked second, with accuracy ranging from 63% to 80%. Starlink placed lowest overall, although it still recorded competitive results, such as 82% accuracy in the internet speed variable. The application of Random Oversampling improved the model’s classification accuracy by an average of 6–12% compared to the non-oversampling model. This study offers strategic insights into public perception of internet services and can serve as a reference for improving service quality based on data-driven user feedback.
Analisis Sentimen Masyarakat Terhadap Liga Indonesia Menggunakan Algoritma Naïve Bayes Classifier dan Support Vertor Machine Pada Platform X dan YouTube Irwanda, Mahyuda; Afdal, M; Novita, Rice; Zarnelly, Zarnelly
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7294

Abstract

The Indonesian League is a national football competition that attracts a lot of public attention. However, various problems such as controversial referee decisions, fan riots, and match-fixing issues are often in the spotlight. This study aims to analyze public sentiment towards the Indonesian League using the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms. Data were collected from social media platform X (Twitter) as many as 2000 tweets and YouTube as many as 2000 comments in the period from January 2023 to December 2024. After going through preprocessing stages such as cleaning, case folding, tokenizing, stopword removal, and stemming, the data was classified into positive, negative, and neutral sentiments. The results showed that SVM had a higher accuracy (99%) than NBC (85%) in sentiment analysis.
Co-Authors 'alimah, Muta Afifah, Afifah Pendri Afifah, Tiara Afrah Ahsyar, Tengku Khairil Akhiril Anwar Harahap Al-Qadr, Nola Ardelia Al-Yasir, Al-Yasir Alfian, Zhevin Anahyu, Yelfi Dwi Angraini Angraini Anjani, Yulia Merry Anofrizen Anofrizen Ariansah, Rizky Arif Marsal Aulia Rahman, Hafiz Butar Butar, Febiola Siska Dulbari, Dulbari Eki Saputra fitriano, ernesto Hamzah, Muhammad Luthfi Harmutika, Della Inggih Permana Irwanda, Mahyuda Jazman, Muhammad M. Afdal Mega Fatimah Rosana Megawati , Megawati Megawati - Mona Fronita, Mona Munandar, Darwin Munzir, Medyantiwi Rahmawita Mustakim Mustakim Muttakin, Fitriani Nazaf, Latiful Nurfadilla, Nadia Permana, Jeki Harya Pratama, Arya Yendri Priady, Muhamad Ilham Pristiawati, Andani Putri Putra, Ahdika Sawiyya Putra, Tandra Adiyatma Rahman, Eman Rahmawita, Medyantiwi Ramadhani, Indah Razmi, Fikri Rice Novita Rozanda, Nesdi Evrilyan S. Nagalay, Fitra Salam Siti Monalisa Sri Lestari Suwito, Bagus Anjari Syaifuddin, Aditya tri utami, septiana Ula, Walid Alma Widi Nugroho, Handoyo Widyawati, Dewi Kania Wulandari, Suri Yanti, Rahma Zulkarnain Zulkarnain Zuriati, Zuriati