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All Journal Riau Journal of Computer Science Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INOVTEK Polbeng - Seri Informatika JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JOISIE (Journal Of Information Systems And Informatics Engineering) EDUMATIC: Jurnal Pendidikan Informatika Journal of Technopreneurship and Information System (JTIS) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Zonasi: Jurnal Sistem Informasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Journal Zetroem Jurnal Computer Science and Information Technology (CoSciTech) Jurnal J-PEMAS Jurnal Nasional Teknik Elektro dan Teknologi Informasi JAIA - Journal of Artificial Intelligence and Applications Innovative: Journal Of Social Science Research SATIN - Sains dan Teknologi Informasi The Indonesian Journal of Computer Science
Junadhi Junadhi
STMIK Amik Riau
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Other

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Journal : The Indonesian Journal of Computer Science

 1 
Implementation of Model View Controller Architecture in Criticism and Suggestion Applications Using the Object Oriented Analysis and Design Method Said, Shodiq Mufadhol; Junadhi
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3229

Abstract

STMIK Amik Riau is the first computer college in Riau, founded in 1996, and has produced thousands of graduates spread throughout Indonesia. Apart from the many achievements that have been achieved by this campus, in fact, there are still many problems that students often complain about. STMIK Amik Riau itself already has an assessment system, but it is still limited to an assessment of lecturers and services in general which are filled out once in one semester. Complaints submitted also cannot be seen and confirmed by other students so there is no benchmark that becomes a benchmark whether the complaint is worth prioritizing or not. Therefore an application system is needed that aims to provide criticism and suggestions in a transparent manner to improve the quality of the STMIK Amik Riau campus. This application will later provide a filtering feature that will display complaints based on the selected criteria using the Ascending and Descending concepts. MVC is a method used in designing applications with UML as a visual language based on the Software Development Life Cycle concept with an OOAD approach. The tools used in designing this application are Android Studio using the Flutter Framework with Dart as the main language.
Sistem Pendukung Keputusan Kelayakan Sertifikasi Guru Menggunakan Metode Multi Attribute Utility Theory (MAUT) Pada SMAN 2 Mandau Harianto, Helfin; Agustin; Junadhi; Tashid
The Indonesian Journal of Computer Science Vol. 12 No. 2 (2023): 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.v12i2.3169

Abstract

Pelaksanaan sertifikasi guru merupakan komitmen pemerintah untuk mengimplementasikan amanat Undang-Undang Nomor 14 tahun 2005, yakni mewujudkan guru yang berkualitas dan profesional. Hal-hal terkait dengan proses sertifikasi masih belum sepenuhnya menggunakan sistem yaitu masih dengan cara mendata guru yang layak mengikuti proses sertifikasi berdasarkan kriteria masa kerja, usia, pendidikan terakhir, tugas tambahan, prestasi mengajar, dan jumlah jam mengajar sehingga sering kali menimbulkan kesulitan ketika mengusulkan guru yang layak mengikuti proses sertifikasi dikarenakan memakan waktu yang lama saat pengurutan ranking sertifikasi. Banyak guru yang mengeluhkan proses sertifikasi yang tidak transparan, diantaranya guru yang usia muda serta masa kerja yang lebih sedikit mendapat kesempatan lebih dulu menjalani proses sertifikasi daripada guru yang sudah mempunyai pengalaman kerja yang lama dan usia tua. Sistem pendukung keputusan merupakan proses tindakan atau aksi dalam pemecahan masalah yang diyakini akan memberikan solusi terbaik untuk mencapai tujuan. Dalam Sistem Pendukung Keputusan ini digunakan metode Multi Attribute Ultility Theory (MAUT). Hasil penelitian menggunakan metode mampu memberikan rekomendasi guru yang layak mengikuti sertifikasi. Penerapan metode MAUT memberikan hasil akurasi sebesar 90%, dari hasil tersebut menunjukkan bahwa metode MAUT bisa menjadi metode alternatif untuk sistem kelayakan sertifikasi guru.
Penerapan Metode Support Vector Machine Untuk Analisis Sentimen Terhadap Produk Skincare Jasmarizal; Junadhi; Rahmaddeni; M. Khairul Anam
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3654

Abstract

Perawatan kulit telah menjadi aspek yang signifikan dalam pola hidup modern. Kesadaran masyarakat terhadap penampilan dan kesehatan kulit semakin meningkat, mendorong permintaan terus berkembang untuk produk skincare. Konsumen sering menghadapi kesulitan dalam memilih produk yang sesuai dengan jenis kulit mereka, di mana ulasan dari pengguna lain bisa menjadi panduan berharga, namun juga berpotensi menyebabkan kebingungan jika tidak dikelola dengan baik. Mengetahui sentimen konsumen terhadap produk skincare tidak hanya membantu produsen dan pengecer memahami penerimaan produk, tetapi juga memberikan arahan bagi konsumen lain dalam pengambilan keputusan. Kemajuan dalam teknologi analisis sentimen memungkinkan penelitian yang lebih efisien dan akurat terhadap pandangan konsumen mengenai produk skincare. Analisis sentimen dapat dijalankan secara otomatis menggunakan algoritma dan model kecerdasan buatan, di mana Support Vector Machine (SVM) menjadi salah satu metode yang efektif dalam permasalahan klasifikasi. SVM memberikan wawasan mendalam mengenai sentimen yang terkandung dalam ulasan konsumen. Dataset yang digunakan mengandung komentar dan ulasan dari pengguna terkait produk skincare MS Glow, dengan total 3.006 data. Proses selanjutnya melibatkan tahap pre-processing data, yang mencakup langkah-langkah seperti Case Folding, Normalisasi Data, Tokenisasi, Filtrasi Stop Words, dan Stemming. Pada tahap pemodelan, SVM digunakan untuk mengklasifikasi sentimen atau opini pengguna terhadap produk skincare tersebut. Hasil akhir menunjukkan bahwa model dengan ketidakseimbangan kelas mengalami overfitting, di mana performa model optimal hanya pada data pelatihan dan kurang efektif pada data uji. Namun, dengan melatih model menggunakan kelas yang seimbang dan menerapkan teknik SMOTE, ditemukan hasil optimal, mencapai akurasi sebesar 99.60% dan nilai f1-score sebesar 98.55%.
Optimization of Deep Learning with FastText for Sentiment Analysis of the SIREKAP 2024 Application Handoko; Junadhi; Triyani Arita Fitri; Agustin
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): 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.v14i2.4809

Abstract

This study analyzes public sentiment towards the SIREKAP 2024 application using deep learning. Data was collected from Google Playstore reviews and processed through cleaning, tokenization, and stemming. Word embedding was performed using FastText to capture more accurate word representations, including OOV words. The deep learning models compared were CNN, BiLSTM, and BiGRU. Performance evaluation used accuracy, precision, recall, and F1-score metrics. The results showed that the CNN model with FastText Gensim embedding achieved the highest accuracy of 95.98%, outperforming BiLSTM and BiGRU. This model was more effective in classifying positive and negative sentiments. This study provides insights for developers to improve the performance and public trust in SIREKAP 2024 and opens opportunities for further research with more complex embedding approaches and deep learning models.
Heart Failure Disease Classification Using Random Forest Algorithm with Grid Search Cross Validation Technique Septia, Rapindra; Junadhi; Susi Erlinda; Wirta Agustin
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): 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.v14i2.4765

Abstract

Heart failure is one of the leading causes of death worldwide and requires early detection to reduce the risk of serious complications. However, the imbalance in medical data poses a challenge in developing accurate prediction models. This study developed a heart failure classification model using the Random Forest algorithm, optimized with Grid Search Cross Validation to find the best combination of hyperparameters. The dataset consisted of 300 observations with 12 medical features and 1 target feature. Data preprocessing included outlier removal using the Interquartile Range (IQR) and Winsorize methods. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance, resulting in a more balanced training data distribution. The dataset was split into 80% training and 20% testing data using stratified sampling to maintain class proportions. The model was evaluated using accuracy, precision, recall, and F1-score metrics, with results showing 90% accuracy, 0.94 precision for class 0, 0.80 precision for class 1, 0.91 recall for class 0, and 0.86 recall for class 1. The model was implemented in a Streamlit-based application, allowing users to input health parameters and receive interactive predictions. This study demonstrates that optimizing the Random Forest algorithm with Grid Search Cross Validation can improve heart failure classification performance, providing a practical solution for supporting heart failure classification. Keywords: Heart Failure Classification, Random Forest, Hyperparameter Optimization, SMOTE, Model Evaluation.
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 (IJCS)
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.
Klasifikasi Emosi Terhadap Konflik Israel-Palestina Menggunakan Algoritma Gated Recurrent Unit Saputra, Eko Ikhwan; Fatdha, T.Sy. Eiva; Agustin; Junadhi; M. Khairul Anam
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4106

Abstract

The Israel-Palestine conflict intensified following the October 7, 2023, attack by Hamas on Israel, triggering various emotional reactions on social media. Emotion classification is crucial for understanding public sentiment related to this conflict. This study utilizes 9,917 tweets from platform X (Twitter) to classify emotions such as joy, sadness, anger, fear, disgust, and surprise. The deep learning algorithm used is Gated Recurrent Unit (GRU), developed with three different training and testing data splits: 70:30, 80:20, and 90:10. For text representation, Global Vector (GloVe) word embedding is employed. Given the imbalanced dataset, this study applies the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The research results indicate that the GRU model with a 90:10 data split without using SMOTE achieves the highest accuracy of 75%, followed by the models with 70:30 and 80:20 splits, which each have an accuracy of 73%.
Co-Authors Adhitya Karel Maulaya Agustin Agustin Agustin -, Agustin Agustin Agustin Ahmad Nazri aisyah manurung Alexsander Anam, M Khairul Aprilita, Windi Aprillian Kartino Azdar, Qowiyyu Benino Giordiola Millenio Bias Arbi Fauzan Chakra Wibawanto Devi Efriadi Dwiansyah, Zovanli Edwar Ali Efrizoni, Lusiana Erlin Fatdha, T.Sy. Eiva Fathur Rahman Fransiskus Zoromi Fransiskus Zoromi Friadi, Roby Ginting, Alex Elanta Hadi Asnal, Hadi Handoko Harianto, Helfin Hasibuan, Fadliansyah Helda Yenni, Helda Helfin Harianto Heru Satria Surya Herwin Herwin Herwin, Herwin Irfansyah Irfansyah Jabbar, Fiqri Abdul Jamaris, Muhamad Jasmarizal Laili Suryati Lusiana M. Agustian Reyza Novris M. Jamaris M. Jamaris Mahesya, Abdan Fitra Mardainis Marlim, Yulvia Nora Mi`rajul Rifqi Muhammad Muadin Muhammad Oase Ansharullah Muhammad Oase Ansharullah Muhammad Syaifullah Nanang Susanto Naufal Khairi Nelianli Yan Jaya Nova Agustina Rahmaddeni Rahmaddeni Rahmiati Rahmiati Ramadhani, Yurifa Rio Agus Panbudi Rivaldi Dwi Andhika Rizaldinata, Wahyu Roby Friadi Said, Shodiq Mufadhol Saputra, Eko Ikhwan Septia, Rapindra Setiawan, Hengki Shodiq Mufadhol Said Silvyana Dwi Putri SUSANTI Susanti, Susanti Susi Erlinda SUSI ERLINDA T. Sy. Eiva Fatdha Tashid Tashid Triyani Arita Fitri Wirta Agustin Yansyah Saputra Wijaya Yuda Irawan