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All Journal JUSIFO : Jurnal Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Briliant: Jurnal Riset dan Konseptual Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Teknik Informatika (JUTIF) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pendidikan dan Teknologi Indonesia CONSEN: Indonesian Journal of Community Services and Engagement
Muttakin, Fitriani
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics 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.
Analisis Customer Lifetime Value Berdasarkan Produk Menggunakan Metode RFM/P dan Algoritma Fuzzy C-Means Rachmawati, Dyana; Monalisa, Siti; Muttakin, Fitriani
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6320

Abstract

212 Mart Soebrantas is a retail company based on a Sharia Cooperative. 212 Mart Soebrantas segments its customers in terms of monetary value, specifically customers who make many purchases. Currently, 212 Mart does not consider recency and frequency, because customers who make transactions of 50 thousand rupiahs receive 1 point. If the points accumulate to 200, they exchange them for a shopping voucher worth 50 thousand rupiah to shop at 212 Mart. 212 Mart Soebrantas needs to understand Customer Lifetime Value (CLV) to determine the customer categories worth keeping and profitable for 212 Mart. Therefore, 212 Mart needs to understand and know its customer segments based on product-based transactions or RFM/P. This research analyzes Customer Lifetime Value Based on Products Using the RFM/P Method and Fuzzy C-Means Algorithm at 212 Mart Soebrantas to help 212 Mart identify customer segment characteristics, and customer loyalty per product category, and provide strategic recommendations. The data used is customer transaction data from January 2023 to September 2023. The study uses products from 10 categories with 6 attributes: Member Code, Stock Name, Transaction Date, Quantity, Basic Price, and Department. The research shows that the best cluster is found in the Basic Material category with a DBI value of 0.4990, and it is a Superstar Customer based on Customer Portfolio Analysis (CPA).
Analisis Sentimen Komentar Perplexity AI di X Tentang Pendidikan Menggunakan Support Vector Machine Ardiansah, Yoga; Monalisa, Siti; Muttakin, Fitriani
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6396

Abstract

Chatbots with Artificial Intelligence are increasingly popular in everyday life. Due to its ability to reason and convey information expressively, Artificial Intelligence (AI) using Natural Language Processing (NLP) models can communicate like humans. Users find one of Perplexity's AI chatbots interesting because it can pinpoint sources of information. As time goes by and the number of Perplexity users increases, sentiment analysis is used to measure user happiness. This sentiment analysis serves as the data source for this research, helping understand how users react to social media X (Twitter). The Support Vector Machines (SVM) method was used in this study, where SVM maximises the distance (margin) between data groups to determine the ideal hyperplane. According to the survey, 90.11% of respondents expressed positive sentiments, 5.30% expressed negative opinions, and 4.69% expressed neutral sentiments. Using a ratio of 80% training data and 20% test data, the f1 score reached 96%, with accuracy and precision of 92% each.
Analisa Sentimen Pengguna Aplikasi DANA Pada Ulasan Google Play Store Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neighbors Sabillah, Dian Ayu; Afdal, M; Permana, Inggih; Muttakin, Fitriani
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.v7i2.7861

Abstract

The use of digital wallets such as DANA in Indonesia continues to increase along with the need for fast and practical non-cash transactions. User reviews on the Google Play Store are an important source of information to assess satisfaction and service problems. This study aims to classify user sentiment towards the DANA application using the Naïve Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. A total of 1,000 reviews were collected and processed through text cleaning, tokenization, stopword removal, and stemming. Sentiments were classified into positive, neutral, and negative using the lexicon method and expert validation. The results showed that NBC was superior to KNN, with the highest accuracy of 71.83%, while KNN only reached 56.44%. NBC was also more effective in detecting negative sentiment, although both were less than optimal for neutral sentiment. Word cloud visualization displays the dominant words in each sentiment category. The conclusion of this study states that Naïve Bayes is more effective in analyzing sentiment reviews of digital wallet applications such as DANA.
Perbandingan Algoritma LSTM, Bi-LSTM, GRU, dan Bi-GRU untuk Prediksi Harga Saham Berbasis Deep Learning Tshamaroh, Muthia; Permana, Inggih; Salisah, Febi Nur; Muttakin, Fitriani; Afdal, M
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.v7i1.7252

Abstract

Stock price prediction is an important component in making investment decisions. This study aims to compare the performance of four deep learning models, namely LSTM, Bi-LSTM, GRU, and Bi-GRU, in predicting stock prices, in order to find the most optimal model for the implementation of an accurate stock price prediction system. Five years of historical data undergoes normalization, windowing, and is separated into training data, validation data, and test data. Model training is conducted with different settings of batch size, timestep, and three kinds of optimizers (Adam, SGD, RMSprop). Performance assessment employs MSE, RMSE, MAE, and R² measurements. The findings indicate that the Bi-GRU model utilizing Adam optimizer settings, a batch size of 8, and a timestep of 21 yields the highest performance, achieving an MSE of 0.0003, an RMSE of 0.0169, an MAE of 0.0129, and an R² of 0.9438. This model demonstrates a strong capability to identify intricate patterns and long-term temporal relationships, outperforming other models in accuracy. The results advocate for the establishment of a predictive system that aids investors and firms in making strategic decisions based on data.
Co-Authors Abdilllah, Abdilllah Andrika, Nadila Angraini Angraini Anjani, Yulia Merry Ardiansah, Yoga Ariansah, Rizky Arif Marsal Auliadi, Maulana Dermawan, Tri Efendi, Harisman Eki Saputra Febi Nur Salisah Fitri Amillia Hamzah, Muhammad Luthfi Inggih Permana Intan, Sofia Fulvi Jazman, Muhammad M. Afdal Maheri, Rahman Megawati - Mona Fronita, Mona Mubarak MR, Najmuddin Muhammad Fadli Mulyanto Mulyanto Nabillah, Putri Nur Sarifudin, Muhammad Pratama, Arya Yendri Pristiawati, Andani Putri Putri, Amanda Iksanul Rachmawati, Dyana Rahmawati, Rahmawati Rahmawita Munzir, Medyantiwi Sabillah, Dian Ayu Safrudin, Muhammad Salsabila, Salsabila Sarbaini Sarbaini Siregar, Muslim Putra Perdana Siti Monalisa Syahri, Alfi Tasia, Ena Tengku Khairil Ahsyar Tshamaroh, Muthia Zarnelly Zarnelly