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All Journal JTT (Jurnal Teknologi Terpadu) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika ILKOM Jurnal Ilmiah Voice Of Informatics AL-ULUM: JURNAL SAINS DAN TEKNOLOGI JOISIE (Journal Of Information Systems And Informatics Engineering) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Sistem Informasi dan Informatika (SIMIKA) JSR : Jaringan Sistem Informasi Robotik Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Ilmu Komputer Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Jurnal J-PEMAS JAIA - Journal of Artificial Intelligence and Applications SATIN - Sains dan Teknologi Informasi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer The Indonesian Journal of Computer Science
Hadi Asnal, Hadi
STMIK Amik Riau
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering

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ANALISIS PERBANDINGAN ALGORITMA C4.5 DAN NAIVE BAYES UNTUK MEMPREDIKSI KETERCAPAIAN TARGET PO DALAM MEMBANGUN PROJECT FTTH (FIBER TO THE HOME) Pratama, Ahmad Tara; Deni, Rahmad; Agustin, Agustin; Asnal, Hadi
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2309

Abstract

In the digital era, the demand for high-speed and stable internet has become essential to support communication and information access. Fiber to the Home (FTTH) is one of the main solutions implemented by internet service providers such as MyRepublic. A critical component in FTTH network development is the issuance of Purchase Orders (PO) to vendors, which directly impacts the achievement of sales targets. This study aims to compare the performance of the C4.5 and Naïve Bayes classification algorithms in predicting PO target achievement to assist project planning and decision-making. The research uses historical data from FTTH projects and applies data partitioning scenarios of 70:30, 80:20, and 90:10 for model training and testing. Evaluation was conducted using accuracy as the main performance metric. The results show that the Naïve Bayes algorithm achieved the highest accuracy of 85.64% with a 70:30 data split, while C4.5 obtained 83.54% accuracy with a 90:10 data split. Based on these findings, the Naïve Bayes algorithm is considered more effective and consistent in predicting PO target achievement and is recommended for implementation in similar project scenarios.
Adaptive Neural Collaborative Filtering with Textual Review Integration for Enhanced User Experience in Digital Platforms Efrizoni, Lusiana; Ali, Edwar; Asnal, Hadi; Junadhi, Junadhi
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.944

Abstract

This research proposes a hybrid rating prediction model that integrates Neural Collaborative Filtering (NCF), Long Short-Term Memory (LSTM), and semantic analysis through Natural Language Processing (NLP) to enhance recommendation accuracy. The main objective is to improve alignment between system predictions and actual user preferences by leveraging multi-source information from the Amazon Movies and TV dataset, which includes explicit user–item ratings and textual reviews. The core idea is to combine three complementary processing paths—(1) user–item interaction modeling via NCF, (2) temporal dynamics capture through LSTM, and (3) semantic understanding of reviews using NLP—into a unified deep learning-based adaptive architecture. Experimental evaluation demonstrates that this multi-input approach outperforms the baseline collaborative filtering model, with the Mean Absolute Error (MAE) reduced from 1.3201 to 1.2817 (a 2.91% improvement) and the Mean Squared Error (MSE) reduced from 2.2315 to 2.1894 (a 1.89% improvement). Training metrics visualization further shows a stable convergence pattern, with the MAE gap between training and validation consistently below 0.03, indicating minimal overfitting. The findings confirm that integrating cross-dimensional signals significantly enhances predictive performance and can contribute to increased user satisfaction and engagement in recommendation platforms. The novelty of this work lies in the simultaneous integration of interaction, temporal, and semantic dimensions into a single adaptive recommendation framework, a configuration not jointly explored in prior studies. Moreover, the flexible architecture enables adaptation to other domains such as e-commerce, music, or online learning, broadening its practical applicability.
SISTEM REKOMENDASI VIDEO GAME BERBASIS USIA SEBAGAI ALAT PENGAWASAN ORANG TUA DI PLATFORM STEAM MENGGUNAKAN CONTENT-BASED FILTERING Oktavianda Oktavianda; Lusiana Efrizoni; Eiva Fatdha; Hadi Asnal
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.4002

Abstract

Video recreations are a prevalent shape of amusement, particularly among children. In any case, numerous parents in Indonesia still need understanding of age appraisals for video recreations, driving to less viable supervision. This could uncover children to unseemly substance. This think about points to create an age-based video amusement suggestion framework utilizing the Content-Based Filtering strategy on the Steam dataset. The framework is planned to help guardians in selecting recreations suitable for their children. Evaluation results show the model performs very well, achieving a precision of 0.98 and a recall of 1.00. Additionally, the model records a Mean Absolute Error (MAE) of 0.469236, Mean Squared Error (MSE) of 6.440935, and Root Mean Squared Error (RMSE) of 2.537900. These findings highlight how well the system filters and suggests age-appropriate video games, assisting parents in better monitoring their kids' gaming habits.
Prediksi Emisi Co2  Di Indonesia Menggunakan Pendekatan Hybrid Arima Dan LSTM Syarifuddin Elmi; Rini Yanti; Mardainis; Hadi asnal
BETRIK Vol. 16 No. 02 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/vtjtfp90

Abstract

Climate change has emerged as a pressing global issue, with carbon dioxide (CO2) emissions serving as a major contributor to global warming. In Indonesia, the expansion of industrial activities, transportation, and the reliance on fossil fuel-based energy have significantly accelerated CO2 emission levels. In this context, the need for accurate emission forecasting has become increasingly important as a basis for formulating data-driven mitigation policies. This study aims to develop a predictive model for CO2 emissions in Indonesia using a hybrid approach that combines AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) methods. ARIMA is employed to capture linear patterns in historical time series data, while LSTM is used to model the non-linear and complex dynamics often present in environmental data. The emission data used spans from 1970 to 2023, with training and testing data separated chronologically in an 80:20 ratio. The evaluation results show that the ARIMA model alone yielded suboptimal performance (RMSE: 2342.5139, MAE: 2341.5775, MAPE: 414.77%), whereas the LSTM model significantly improved prediction accuracy (RMSE: 49.3307, MAE: 45.5498, MAPE: 7.94%). The hybrid ARIMALSTM model achieved the best results, with an RMSE of 31.5778, MAE of 25.0335, and MAPE of 4.34%. These findings indicate that the combination of both methods substantially enhances prediction performance compared to standalone models. The implications of this research are twofold: academically, it contributes to methodological development in environmental data analysis; practically, it offers valuable insights for policymakers in formulating more effective and sustainable carbon emission reduction strategies in Indonesia. 
Perancangan Aplikasi Rekapan Bukti Pembayaran Uang Sekolah Berbasis Mobile Arifandi, Fajar; Mardainis; Asnal, Hadi; Zoromi, Fransiskus
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3761

Abstract

SMK Taruna Pekanbaru merupakan salah satu sekolah yang berada di kota Pekanbaru dengan jumlah siswa/i aktif sekitar 800 orang siswa yang bersekolah di SMK Taruna Pekanbaru. Pada saat ini untuk informasi pembayaran uang sekolah di SMK Taruna Pekanbaru masih dilakukan secara manual sehingga apabila siswa ingin melihat bukti pembayaran sekolah siswa bisa melihat melalui kwitansi dan buku pembayaran. Oleh karena itu, penulis tertarik untuk membahas sistem informasi pembayaran sekolah yang bertujuan memudahkan siswa maupun bagian keuangan salah satunya untuk mengakses informasi pada suatu layanan di sekolah, khususnya pada layanan informasi rekapan bukti pembayaran uang sekolah di SMK Taruna Pekanbaru. Perancangan aplikasi rekapan bukti pembayaran uang sekolah di SMK Taruna Pekanbaru ini merupakan solusi modern yang dirancang untuk mempermudah pengecekan rekapan bukti pembayaran uang sekolah bagi siswa. Metode yang digunakan pada perancangan UI UX Design pada aplikasi rekapan bukti pembayaran uang sekolah di SMK Taruna Pekanbaru menggunakan metode Lean UX dan melakukan testing design menggunakan metode UEQ. Hasil akhir dari perancangan design tersebut akan dirancang kedalam sebuah aplikasi mobile dengan menggunakan bahasa pemograman kotlin. Hal ini dapat disimpulkan bahwa hasil akhir berupa aplikasi informasi pembayaran uang sekolah di SMK Taruna Pekanbaru berbasis mobile, dengan sistem ini siswa dan guru dapat mengetahui informasi pembayaran dan rekapan pembayaran yang mudah dilakukan untuk pengecekan uang sekolah yang sudah di bayar maupun yang belum di bayar, dengan adanya aplikasi ini dapat memudahkan proses administrasi sekolah.
Analisis Sentimen Layanan Hotel Menggunakan Algoritma Extra Trees: Studi Kasus pada Ulasan Pelanggan Aprilita, Windi; Junadhi; Agustin; Hadi Asnal
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4014

Abstract

This research aims to analyze the sentiment of hotel services based on customer reviews using the Extra Trees algorithm. This method was tested on a dataset containing customer reviews about hotel services. The evaluation is done by taking into account the accuracy, precision, recall, and F1 score of the developed model. The results showed that the Extra Trees algorithm was able to achieve an accuracy of 85.05%, with a precision of 84.46%, a recall of 97.00%, and an F1 score of 90.17%. These findings indicate that the Extra Trees algorithm has good performance in analyzing hotel service sentiment based on customer reviews. The implication of this research is to provide guidance to hotels to understand and improve their service quality based on feedback from customers. In addition, this research can also be the basis for further development in the field of sentiment analysis and customer service in the tourism industry.
Co-Authors Agus Alamsyah Agustin Agustin Agusviyanda Agusviyanda Aldi Syahputra Anam, M Khairul Aprilita, Windi Arifandi, Fajar Bayu Yuliansyah Bunga Nanti Pikir Deni, Rahmad Didik Sudyana Edwar Ali Efrizoni, Lusiana Eiva Fatdha Elisawati, Elisawati Erlinda, Susi Fransiskus Zoromi Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Gunadi Gunadi Hamdani Hamdani - Hartomi, Zupri Henra Helda Yenni, Helda Ivan Gunawan Jamaris, Muhamad Jamaris, Muhammad Junadhi Junadhi Junadhi, Junadhi Kadek Mirnawati Lusiana Efrizoni Mardainis Mardainis Marni, Prina Mitrin, Abdullah Mohd Rinaldi Amartha Muhammad Bambang Firdaus Muhammad Febri Devara muhammad Fuad Muhammad Muadin Muhammad Nur Cahyadi Murtadho Habibi Habibi Nasrul Sani Nora Lizarti Nurhadi Oktavianda Oktavianda Pratama, Ahmad Tara Prilly Maya Gita Rahmaddeni Rahmaddeni Rahmiati Rahmiati Rahmiati renaldi, reno Rini Yanti Rini Yanti Rometdo Muzawi, Rometdo Supahri, Hafid Azis Susanti, Mega Susanti, Susanti Susi Erlinda Syarifuddin Elmi T. Sy. Eiva Fatdha Tashid Torkis Nasution Triyani Arita Fitri Ulfah, Aniq Noviciate Waleska, Rangga Febrio Yansyah Saputra Wijaya Yuda Irawan Zaki Mubarok G