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All Journal JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi Tech-E JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Technomedia Journal Jurnal Sistem Cerdas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal Journal of Social Science Jurnal Teknik Informatika (JUTIF) Jurnal Abmas Negeri (JAGRI) Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika Green Intelligent Systems and Applications Jurnal Algoritma Journal of Social Science IWJ : Inland Waterways Journal Information Technology International Journal (ITIJ) The Indonesian Journal of Computer Science Indonesia Berdampak: Jurnal Pengabdian Kepada Masyarakat
Nouval Trezandy Lapatta
Universitas Tadulako
Religion Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Physics Social Sciences Transportation Other

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Implementation of Naive Bayes for Optimizing Asset Condition Classification in a Web-Based Information System Putra, Adhitya Pramana; Lapatta, Nouval Trezandy; Ngemba, Hajra Rasmita
Technomedia Journal Vol 10 No 3 (2026): February
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v10i3.2273

Abstract

Improving the quality of work performance is an essential aspect for employees at the Office of Investment and Integrated One-Stop Services of Central Sulawesi Province. Many challenges remain in managing asset data, especially because the recording and monitoring processes are still performed manually. This manual approach often leads to inconsistencies, inefficiencies, and difficulties in determining asset eligibility. Therefore, an information system capable of supporting accurate and efficient data management is highly needed. The main objective of this study is to apply the Naive Bayes algorithm to classify asset conditions in a web-based system, enabling faster decision-making and improving the accuracy of asset feasibility assessments within government institutions. The dataset used in this study consists of three key attributes asset functionality, asset age, and physical condition. These attributes serve as indicators for classification using the Naïve Bayes probabilistic approach. The developed web-based application was evaluated through black-box testing to ensure that all system functions performed according to expectations and produced consistent outputs. Black-box testing results show that the system successfully provides correct outputs for each test scenario, verifying that the classification and data management processes operate properly. The application is able to classify assets into feasible or non-feasible categories based on calculated probabilities. Findings indicate that implementing the Naïve Bayes algorithm significantly improves the efficiency of asset data processing and enhances data management quality. The system also supports more objective decision-making regarding asset feasibility. This study demonstrates that probabilistic classification can be effectively integrated into governmental asset management systems to optimize operational performance.
Implementation of Long Short-Term Memory Algorithms on Cryptocurrency Price Prediction with High Accuracy on Volatile Assets Nursiana Zasqia, Andi Nirina; Laila, Rahmah; Trezandy Lapatta, Nouval; Yazdi Pusadan, Mohammad; Santi, Dessy; Wirdayanti
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2422

Abstract

Cryptocurrencies have emerged as one of the most popular digital assets, characterized by high volatility, which presents a significant challenge in forecasting their price movements accurately. This study aims to implement the Long Short-Term Memory (LSTM) algorithm to predict the prices of selected cryptocurrencies, including Bitcoin (BTC), Binance Coin (BNB), Ethereum (ETH), Dogecoin (DOGE), Solana (SOL), and Shiba Inu (SHIB). The LSTM model is trained using the Adam optimizer and employs early stopping to mitigate overfitting. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results indicate that the LSTM model achieves strong predictive accuracy for relatively low-volatility assets such as Dogecoin and Solana, with R² scores of 0.9795 and 0.9523, respectively. In contrast, its performance declines when applied to highly volatile assets like Bitcoin and Binance Coin. The findings also suggest that LSTM performs best in short-to-medium-term forecasts (7 to 30 days), but shows limitations in long-term predictions. This study contributes to the field by demonstrating the applicability of LSTM in financial forecasting and highlighting its strengths and constraints across different volatility profiles. Practically, the findings can assist traders and financial analysts in making data-driven decisions by applying LSTM models for more reliable short-term predictions, while emphasizing the need to integrate external market factors to enhance long-term forecast accuracy.
Analisis Sentimen Terhadap Kinerja Awal Pemerintahan Menggunakan IndoBERT Dan SMOTE Pada Media Sosial X Ihalauw, Sahron Angelina; Trezandy Lapatta, Nouval; Wiria Nugraha, Deny; Wirdayanti; Ar Lamasitudju, Chairunnisa
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2957

Abstract

Social media platform X has become a key channel for expressing public opinion on political issues, including evaluating the early performance of the government. The first 100 days of an administration are a strategic period to assess policy direction and public perception. This study aims to apply and evaluate the IndoBERT model for sentiment analysis of Indonesian-language tweets discussing the 100-day performance of the Prabowo–Gibran administration, as well as to assess the impact of using the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalance. A total of 15,027 tweets were collected through API crawling and processed through several stages: preprocessing, labeling using the InSet Lexicon, data splitting, and fine-tuning IndoBERT. Two scenarios were tested — without SMOTE and with SMOTE oversampling. The results show that both models achieved the same overall accuracy of 87%, but performance varied across sentiment classes. The model without SMOTE performed better in the positive class with 93% precision, whereas the SMOTE-applied model improved performance in the neutral class (F1-score increased from 70% to 71%; recall from 69% to 71%) and in the negative class (precision increased from 88% to 90%). Considering the balance across classes, the SMOTE-based model was selected as the final model and implemented into a Streamlit application for interactive sentiment analysis. This study expands the application of IndoBERT in the Indonesian political domain by combining the lexical InSet approach with SMOTE oversampling — a combination rarely applied in Indonesian political sentiment analysis. The findings highlight the importance of data balancing strategies in improving transformer-based model performance on imbalanced datasets. Future research is encouraged to explore alternative balancing methods, expand training data, and test other transformer variants to enhance accuracy and generalization.
Implementasi Smart Housing Services Akses Hunian Berkualitas Melalui Teknologi Digital Ayu Hernita; Nouval Trezandy Lapatta; Sri Khaerawati Nur; Deny Wiria Nugraha
Indonesia Berdampak: Jurnal Pengabdian kepada Masyarakat Vol. 1 No. 2 (2025): JULI-DESEMBER
Publisher : Indo Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63822/0r4aa406

Abstract

Transformasi digital dalam layanan publik menjadi salah satu prioritas dalam meningkatkan efisiensi dan aksesibilitas pelayanan kepada masyarakat, termasuk dalam sektor perumahan. Salah satu solusi inovatif adalah penerapan Smart Housing Services, yaitu sistem layanan digital yang memungkinkan masyarakat mengakses informasi, bantuan, dan fasilitas perumahan secara daring. Namun, pemahaman dan kesiapan instansi pemerintah daerah dalam mengimplementasikan layanan ini masih perlu ditingkatkan. Pengabdian kepada masyarakat ini bertujuan untuk memberikan sosialisasi dan pelatihan mengenai pemanfaatan Smart Housing Services kepada pegawai Dinas Komunikasi dan Informatika Kabupaten Parigi Moutong. Kegiatan ini dilaksanakan dalam bentuk pelatihan satu hari yang mencakup pengenalan konsep layanan perumahan digital, simulasi penggunaan sistem, serta pembuatan media sosialisasi visual berupa infografis dan poster. Peserta juga dibekali dengan modul dan template yang dapat digunakan kembali untuk kegiatan penyebaran informasi kepada masyarakat. Metode pelaksanaan meliputi tahapan persiapan, penyampaian materi, praktik langsung, dan evaluasi hasil. Hasil kegiatan menunjukkan peningkatan pemahaman peserta terhadap konsep Smart Housing Services sebesar 85%, berdasarkan hasil pre-test dan post-test. Selain itu, peserta berhasil menghasilkan media edukatif sederhana yang dapat mendukung upaya literasi digital di bidang layanan perumahan. Kegiatan ini diharapkan menjadi kontribusi awal dalam mendukung perluasan akses hunian berkualitas berbasis teknologi, serta memperkuat peran Dinas Kominfo sebagai motor penggerak transformasi digital di daerah.
Architectural Analysis of the Repository Pattern in Web-Based Credit Score Conversion Assessment System Based on PermenPAN-RB No. 1 of 2023 Lapatta, Nouval Trezandy; Syahrullah
Tech-E Vol. 9 No. 2 (2026): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v9i2.4362

Abstract

PermenPAN-RB Regulation No. 1 of 2023 introduced a major shift in functional position assessment by emphasizing performance predicate conversion in credit score evaluation, which increases architectural demands on supporting information systems. In practice, many assessment systems remain tightly coupled and difficult to evolve when regulatory rules, integration sources, or reporting formats change. This paper presents an architecture-oriented analysis of a web-based credit score conversion assessment information system that applies the Repository Pattern as a core architectural mechanism to decouple business logic from persistence, integration, and document-generation concerns. The analysis adopts a scenario-based evaluation approach inspired by the Architecture Tradeoff Analysis Method (ATAM) and is grounded in the ISO/IEC 25010 software quality model, focusing on maintainability, modifiability, testability, scalability, and reliability. Architectural evaluation is conducted by examining layered boundaries, repository abstractions, and dependency injection mechanisms under representative regulatory-driven change scenarios, including rule adjustments, data integration extensions, and reporting modifications. The results demonstrate consistent change localization across architectural layers, where rule changes are confined to service modules, integration changes are absorbed by repository adapters, and reporting changes remain isolated within document-generation components. These findings show that repository-based architectures significantly reduce coupling, improve change isolation, and support the sustainable evolution of government information systems operating under dynamic regulatory environments.
Recency, Frequency, and Monetary-Based Customer Segmentation Using K-Means for Analysing Transactional Behaviour in a Service-Based Micro, Small, and Medium Enterprises Ardiansyah, Rizka; Trezandy, Nouval; skandar, Iskandar; Ilman, Meilani; Sahril, Sahril
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.919

Abstract

Micro, Small, and Medium Enterprises (MSMEs) often faced challenges in designing effective promotional initiatives due to the limited use of systematic customer behavior analysis. This study examined the application of (Recency, Frequency, Monetary) RFM analysis combined with K-Means clustering to explore customer segmentation in a service-based MSME context. Transaction data from a local laundry service operating in Palu, Indonesia, consisting of 2,220 digital transaction records collected between 2022 and 2025, were processed and transformed into RFM variables using min–max normalization. The optimal number of clusters was determined using the Elbow method, resulting in four customer segments. Cluster quality was evaluated using internal validation metrics, yielding a Davies–Bouldin Index (DBI) of 0.61 and a Sum of Squared Errors (SSE) value of 1.73, indicating reasonably compact and well-separated clusters. The resulting segments exhibited distinct transactional profiles across recency, transaction frequency, and monetary contribution, reflecting heterogeneity in customer engagement within the studied MSME. Rather than prescribing specific marketing actions, the findings provided an interpretable analytical basis for considering differentiated promotional strategies aligned with observed customer behavior patterns. Overall, this study demonstrated that RFM-based segmentation offered a feasible and data-driven approach to supporting evidence-informed promotional planning in service-oriented MSMEs operating under data and resource constraints.
CLUSTERING OF E-WALLET USAGE BASED ON TRANSACTION PATTERNS USING K-MEANS Hernita, Ayu; Lapatta, Nouval Trezandy; Saputra, Sabaruddin; Akbar, Muhammad
Jurnal Pilar Nusa Mandiri Vol. 22 No. 1 (2026): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v22i1.8292

Abstract

The use of e-wallets has grown rapidly along with the increasing demand for fast and conven-ient digital financial transactions. This development requires service providers to better under-stand user behavior through transaction pattern analysis. This study aims to cluster e-wallet users based on their transaction patterns using the K-Means algorithm. The data analyzed in-clude several key variables, namely transaction frequency, transaction value, transaction type, and transaction time. The K-Means method is applied to group users with similar transaction characteristics through data normalization and optimal cluster determination. The results in-dicate that e-wallet users can be divided into several distinct segments with significantly dif-ferent transaction patterns. Each cluster represents specific user behavior characteristics, such as high-frequency users, high-value transactions, or time-based usage patterns. In conclusion, this study contributes to the growing body of research on digital payment analytics by demonstrating the applicability of the K-Means clustering algorithm for transaction-based user segmentation. The findings provide empirical evidence that behavioral transaction data can be systematically structured into meaningful user groups through unsupervised learning techniques. This research extends prior studies by integrating clustering evaluation methods to ensure optimal segmentation results, thereby offering a methodological reference for future studies in fintech data analysis.
KLASIFIKASI JENIS KAYU BERDASARKAN CITRA SERAT KAYU MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Dwimanhendra, Muhammad Rifaldi; Syahrullah, Syahrullah; Joefrie, Yuri Yudhaswana; Angreni, Dwi Shinta; Azhar, Ryfial; Nugraha, Deny Wiria; rezandy Lapatta, Nouval; Najar, Abdul Mahatir
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5726

Abstract

Kayu merupakan sumber daya alam yang sangat penting bagi industri mebel atau furnitur. Pemilihan jenis kayu yang tepat sangat krusial dalam industri mebel untuk menentukan kualitas hasil produksi. Pemilihan kayu secara manual memiliki risiko kesalahan yang dapat berdampak negatif pada kualitas akhir produk mebel. Oleh karena itu, diperlukan penerapan teknologi untuk meminimalkan kesalahan pemilihan jenis kayu dan meningkatkan efisiensi proses produksi. Penelitian ini bertujuan membangun model klasifikasi jenis kayu (nantu, palapi, dan uru) berbasis Convolutional Neural Network (CNN) menggunakan citra serat kayu. Dataset terdiri dari 1.584 citra yang dibagi menjadi 80% data pelatihan dan 20% data pengujian. Arsitektur model CNN terdiri dari 4 lapisan konvolusi, 4 lapisan pooling, dan 2 lapisan fully-connected. Hasil pelatihan mencapai akurasi 97,06%, sedangkan hasil pengujian dan evaluasi menggunakan matriks konfusi mencapai akurasi 95,56%. Penelitian ini membuktikan bahwa CNN dapat digunakan secara efektif untuk klasifikasi jenis kayu dengan tingkat akurasi yang tinggi, sehingga dapat membantu meningkatkan efisiensi proses produksi mebel.
Identifikasi Telur Ayam Fertil dan Infertil Melalui Citra Candling Menggunakan Algoritma Vision Transformer Paloloang, Muhammad Fadhil Akmal B.; Lapatta, Nouval Trezandy Trezandy; Yazdi, Mohammad; Anshori, Yusuf
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6298

Abstract

Telur ayam merupakan salah satu komoditas utama dalam industri peternakan unggas. Pengelolaan telur menjadi masalah yang perlu mendapat perhatian khusus agar terhindar dari kerugian, salah satunya yaitu dengan melakukan pengecekan kesuburan telur. Candling merupakan metode konvensional yang kerap digunakan untuk melakukan pengecekan, akan tetapi rawan akan terjadinya kesalahan dalam mengidentifikasi karena menggunakan cara manual dan tentunya akan menghabiskan waktu. Oleh karena itu diperlukan sebuah sistem untuk dapat membantu peternak dalam melakukan identifikasi secara otomatis guna meningkatkan kualitas produksi telur. Dataset pada penelitian ini menerapkan teknik Contrast Limited Adaptive Histogram Equalization (CLAHE) untuk meningkatkan kontras dan mempertegas pola embrio pada citra telur. Sistem identifikasi dibangun menggunakan  algoritma Vision Transformer (ViT), yang merupakan arsitektur berbasis Transformer yang dapat memahami hubungan atau relasi diantara berbagai bagian gambar. Dataset terbagi atas dua kelas dengan total keseluruhan dataset berjumlah 228 citra, dengan hasil akurasi pelatihan yang didapatkan sebesar 99,77% dan akurasi validasi sebesar 98,03%. Pengujian confusion matrix dan ROC AUC dilakukan menggunakan data baru diluar dari data pelatihan, model mampu menentukan kelas telur fertil dan infertil dengan baik, dengan nilai akurasi dan recall sebesar 95%, dan nilai ROC AUC sebesar 99,68%. Hasil dari penelitian ini dapat membantu dan mempermudah peternak dalam mengidentifikasi telur fertil dan infertil agar kualitas produksi telur senantiasa terjaga dan dapat memastikan bahwa kondisi telur tetap berkembang.
ANALISIS SENTIMEN MASYARAKAT DI MEDIA SOSIAL X TERHADAP MASALAH ROHINGYA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN TEKNIK RESAMPLING Krama, Tri; Lapatta, Nouval Trezandy; Lamasitudju, Chairunnisa Ar.; Syahrullah, Syahrullah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6193

Abstract

Di era modern ini, penggunaan media sosial terus meningkat karena kemajuan dalam teknologi informasi dan komunikasi. X, sebagai salah satu platform media sosial populer, memungkinkan pengguna untuk berinteraksi dan berdiskusi mengenai berbagai isu, termasuk masalah pengungsi Rohingya. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap isu Rohingya di X menggunakan algoritma Support Vector Machine (SVM) dan teknik resampling untuk mengatasi ketidakseimbangan data. Metode yang digunakan meliputi oversampling dengan Synthetic Minority Over-sampling Technique (SMOTE) dan random undersampling untuk menyeimbangkan dataset. Hasil penelitian menunjukkan bahwa setelah penerapan SMOTE dan random undersampling, model SVM mencapai akurasi 89% dengan precision 0,9, recall 0,89, dan f1-score 0,89. Selain itu, penggunaan seleksi fitur dengan chi-square juga terbukti efektif dalam meningkatkan akurasi model, meskipun peningkatannya sedikit, yaitu dari 89% menjadi 90%.Temuan ini menekankan pentingnya penggunaan teknik resampling dan seleksi fitur yang tepat dalam analisis sentimen sosial yang kompleks, khususnya dalam konteks isu pengungsi Rohingya di platform X.
Co-Authors ., Rezki Abdillah Sani, Ilham Abdul Mahatir Najar Abdullah Abdullah Adhira Putri, Dhivanny Agung Stiven Cahyati Angely Ain, Moch. Zukhruf Amriana Amriana Amriana Amriana Andhyka, Andhyka Andi Hendra Andi Hendra Angraeni, Dwi Shinta Anita Ahmad Kasim Anita, Ayu Arsita, Tiara Juli Asriani Asriani, Asriani Ayu Hernita Bakri Chandra, Ferri Rama Darojah, Murtafiatun Delia, Fenita Deni Luvi Jayanto Deny Wiria Nugraha Dessy Santi Djohari, Riyandi Dwitama Dwi Shinta Angreni Dwimanhendra, Muhammad Rifaldi Fahlevi, Mohammad Fazrin Fajar, Moh Fajriyah, Nurul Faldiansyah, Faldiansyah Firzatullah, Raden Muhamad Hajra Rasmita Ngemba Hamid, Odai Amer Hanama, Ikhsan Wahyudin Ihalauw, Sahron Angelina Ihwan, Abib Raifmuaffah Ilman, Meilani Karnita Sumbaluwu, Harlin Feby Kartika, Rina Krama, Tri Laila, Rahma Lamadjido, Moh. Raihan Dirga Putra Lamasitudju, Chairunnisa Mandra Maulana, Muhammad Syahputra Mohamad Irfan, Mohamad Mohammad Yazdi Pusadan Muhammad Akbar Muhammad Akbar Mutiara Sari Ngemba, Hajra Ningsih, Alief Surya Noel Marcell Jonathan Wongkar Noviantika, Noviantika Nurhikmah Supardi Nursiana Zasqia, Andi Nirina Pagiu, Harry T. Paloloang, Muhammad Fadhil Akmal B. Priska, Salsa Dilah Putra, Adhitya Pramana Qofifa, Sitti Nurlaili Rahmah Laila Rasmita Ngemba, Hajra Rasmita, Hajra Rasyid, Muh. Ashari Rinianty Rinianty Rinianty, Rinianty Rizka Ardiansyah Rizky, Moh Taufiq Ryfial Azhar Saada, Rahmadian A. Sabarudin Saputra Saputra, Sabaruddin Septiano Anggun Pratama Setiawan, Dita Widayanti Siswahyudianto Siti Rahmawati skandar, Iskandar Sri Khaerawati Nur Sukirman Sukirman Syahrullah Syahrullah Syahrullah Syaiful Hendra Wirdayanti Wirdayanti Wirdayanti Wongkar, Noel Marcell Jonathan Yanti, Wirda Yuri Yudhaswana Joefrie Yusuf Anshori Zulkifli Zulkifli