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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Jurnal Pengabdian UntukMu NegeRI SISFOTENIKA INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi IT JOURNAL RESEARCH AND DEVELOPMENT Indonesian Journal of Artificial Intelligence and Data Mining MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JOISIE (Journal Of Information Systems And Informatics Engineering) Zonasi: Jurnal Sistem Informasi Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Jurnal J-PEMAS Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) J-COSCIS : Journal of Computer Science Community Service JAIA - Journal of Artificial Intelligence and Applications SATIN - Sains dan Teknologi Informasi The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Wirta Agustin
STMIK AMIK Riau
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences

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Implementation of The Open Group Architecture Framework to See the Readiness of Smart Schools in Pekanbaru Anam, M. Khairul; Hendrawan, Riki; Arita Fitri, Triyani; Agustin, Wirta; Zamsuri, Ahmad
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 14 No. 2 (2023): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v14i2.14916

Abstract

Smart Schools are a derivative of smart people in the 6 pillars of Smart City. Smart Schools is also a school concept utilizing information technology used in the teaching and learning process in the classroom and school administration. One of the schools in Pekanbaru City that will implement smart schools is Junior High School 17 Pekanbaru. Currently, the school already has several infrastructures including servers, laboratories, and administrative rooms, but it is necessary to analyze the technology architecture aimed at implementing Smart Schools. The technological architecture would be analyzed using TOGAF (The Open Group Architecture Framework) Framework version 9. The TOGAF framework is a framework for enterprise architecture which is able to develop an enterprise architecture in an organization. Enterprise architecture is an explicit explanation and current documentation of the relationship between management, business processes, and information technology. This research describes the current architectural conditions and target architectures to include the rules, standards, and lifecycle of information systems to optimize and maintain the environment of organizations that want to create and maintain by managing the IT portfolios. The results of this study are to produce an IT blueprint that is used as a school guide in implementing technology architecture to support the implementation of Smart Schools in Pekanbaru City.
Product Classification Based on Categories and Customer Interests on the Shopee Marketplace Using the Naïve Bayes Method Muhammad Oase Ansharullah; Wirta Agustin; Lusiana; Junadhi; Susi Erlinda; Fransiskus Zoromi
JAIA - Journal of Artificial Intelligence and Applications Vol. 2 No. 2 (2022): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v2i2.888

Abstract

Marketplace is an electronic product marketing platform that brings together many sellers and buyers to transact with each other. The large variety of products sold on Shopee is one of the reasons this application is in great demand by all walks of life. However, the weakness of the large variety of products sold in a marketplace causes buyers who have no potential to buy these products. To overcome this problem, it is necessary to do a classification to determine which products are most in demand by customers. Product categories consist of: Clothing, Beauty Products, Daily Goods, Electronics, and Accessories. The classification method used is Naïve Bayes and the software used is WEKA. The next data collection is done by distributing questionnaires to the existing customers on social media namely, Whatsapp and Instagram, the distribution of the questionnaire is conducted through Google form. There are 90 questionnaires that will be distributed in this study. Some of the indicators asked in the questionnaire namely, do you like shopping online? And what marketplaces are commonly used. These results will be the training data. Interest categories are divided into 4 categories, namely: Very interested, Interested, Not interested, Very not interested. The results obtained in this study are clothing products (72 respondents) are products that are in great demand, daily goods products (7 respondents) are products of interest, beauty and electronic products (5 respondents) are products that are not in demand, and accessories (1 respondents ) is a product that is not very attractive to customers on the Shopee marketplace
Improved Performance of Hybrid GRU-BiLSTM for Detection Emotion on Twitter Dataset Anam, M. Khairul; Munawir, Munawir; Efrizoni, Lusiana; Fadillah, Nurul; Agustin, Wirta; Syahputra, Irwanda; Lestari, Tri Putri; Firdaus, Muhammad Bambang; Lathifah, Lathifah; Sari, Atalya Kurnia
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.459

Abstract

This study addresses emotion detection challenges in tweets, focusing on contextual understanding and class imbalance. A novel hybrid deep learning architecture combining GRU-BiLSTM with SMOTE is proposed to enhance classification performance on an Israel-Palestine conflict dataset. The dataset contains 40,000 tweets labeled with six emotions: anger, disgust, fear, joy, sadness, and surprise. SMOTE effectively balances the dataset, improving model fairness in detecting minority classes. Experimental results show that the GRU-BiLSTM hybrid with an 80:20 data split achieves the highest accuracy of 89%, surpassing BiLSTM alone, which obtained 88%, and other state-of-the-art models. Notably, the proposed model delivers significant improvement in detecting the emotion of joy (recall: 0.87, F1-score: 0.86). In contrast, the surprise category remains challenging (recall: 0.24). Compared to existing research, this study highlights the effectiveness of combining SMOTE and hybrid GRU-BiLSTM, outperforming models such as CNN, GRU, and LSTM on similar datasets. The incorporation of GloVe embeddings enhances contextual word representations, enabling nuanced emotion detection even in sarcastic or ambiguous texts. The novelty lies in addressing class imbalance systematically with SMOTE and leveraging GRU-BiLSTM's complementary strengths, yielding superior performance metrics. This approach contributes to advancing emotion detection tasks, especially in conflict-related social media data, by offering a robust, context-sensitive, and balanced classification method.
Opinion Mining menggunakan Algoritma Deep Learning untuk Menganalisis Penggunaan Aplikasi Jamsostek Mobile Azhari, Zahra; Efrizoni, Lusiana; Agustin, Wirta; Yanti, Rini
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.v12i2.3185

Abstract

BPJS Ketenagakerjaan berperan dalam menjaga kesejahteraan para pekerja dan buruh melalui program-program pendidikan dan pelatihan yang diberikan, pelayanan menjadi prioritas terhadap pelanggan untuk memberikan kenyamanan. Melalui aplikasi Jamsostek Mobile yang terdapat di google playstore akan diambil komentar-komentar untuk mendapatkan respon pelanggan terhadap aplikasi Jamsostek mobile untuk dilakukan opinion mining. Komentar yang diambil dari google playstore menggunakan bantuan googleplayscraper, sebanyak 3000 komentar berhasil diambil yang kemudian akan dilakukan tahap pembersihan data, pelabelan, pembobotan kata menggunakan word2vec 300 dimensi dan dilanjutkan menggunakan algoritma Long Short Term Memory. Hasil opinion mining menunjukkan dominasi sentimen negatif sebesar 80.58% dan 19.42% positif dengan tingkat akurasi terbaik yang dihasilkan oleh algoritma LSTM sebesar 87.36%. Hasil penelitian ini akan memberikan wawasan yang berguna bagi pengembang aplikasi untuk meningkatkan kualitas pelayanan dan pengalaman pengguna.
Togaf Analysis in Bengkalis State Polytechnic Laboratory Information Systems Design Agustin, Wirta; Rahmaddeni; Rio, Unang; Suhada, Khairus
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/0hz5q194

Abstract

Laboratories in educational institutions have an important role in supporting learning and research. However, effective and efficient laboratory management is still a challenge, especially in recording inventory and managing consumables (BHP). The Bengkalis State Polytechnic Informatics Engineering Laboratory already has an application system, but there are still limitations in recording monthly BHP usage, borrowing facilities, and proposing equipment procurement. This research aims to design a blueprint for an integrated laboratory information system using The Open Group Architecture Framework (TOGAF) Architecture Development Method (ADM). This approach is applied to phase F: Migration Planning, which includes requirements analysis, business architecture design, information system architecture, as well as strategy and migration implementation. The results of this research produce a blueprint for a system information laboratory that includes application design, technology recommendations, and implementation stages that can be used as a guide in system development. This blueprint is expected to increase laboratory management efficiency by optimizing inventory recording, procurement planning and maintenance of laboratory services. In addition, the TOGAF ADM approach used can be adapted for laboratories in other educational settings that have similar needs.
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.
The Application of Na ve Bayes Classifier Based Feature Selection on Analysis of Online Learning Sentiment in Online Media Putra, Ryanda Satria; Agustin, Wirta; Anam, M. Khairul; Lusiana, Lusiana; Yaakub, Saleh
Jurnal Transformatika Vol. 20 No. 1 (2022): July 2022
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v20i1.5144

Abstract

There are problems that still exist in online learning including limited-reach networks, inadequate facilities and infrastructure, and others. This study discussed the analysis of sentiment which used the Na ve Bayes Classifier (NBC) method with XGBoost feature selection as a performance improvement that took data from news portals. The results of this study showed that graph data on the application of online learning forms in Indonesia had a "Negative" opinion. Performance testing of the NBC method based on XGBoost feature selection was conducted four times. The first experiment resulted in an accuracy value of 60.18% with 50/50 split data. The next experiment had an accuracy value of 56.92% with 70/30 split data. After that, the third experiment resulted in an accuracy value of 65.90% with 80/20 split data. The result of the last experiment was an accuracy value of 63.63% with 90/10 split data. After using XGBoost feature selection, it produced an accuracy of 60.18%, 67.69%, 70.45%, and 77.27%. The study also produced the highest average score at 10-Fold Cross-Validation in the second trial with a score of 65.62%.
PENERAPAN GRADIENT BOOSTING MACHINES UNTUK MEMPREDIKSI PROMOSI JABATAN KARYAWAN Kurniawan, Fadly; Tashid, Tashid; Unang Rio, Unang Rio; Agustin, Wirta
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.v8i1.2307

Abstract

Job promotion is an important factor in human resource management as it can enhance employee motivation, loyalty, and performance. This study aims to build a job promotion prediction model using the Gradient Boosting Machines (GBM) algorithm implemented in RapidMiner Studio. The dataset used was sourced from Kaggle, consisting of 54,808 training records and 23,491 testing records. The research process included data preprocessing, splitting into training and testing sets, model training, performance evaluation using metrics such as accuracy, precision, recall, F1-score, and AUC, and applying the model to actual test data. The developed GBM model achieved an accuracy of 91.10% and an AUC value of 0.776. The prediction results on the test data indicated that approximately 84.4% of employees were predicted as not eligible for promotion, while 15.6% were predicted as eligible. These findings demonstrate that a machine learning approach can help companies make job promotion decisions more objectively, transparently, and data-driven.
Analisis Pilkada Medan pada Sosial Media Menggunakan Analisis Sentimen dan Social Network Analyisis Anam, M. Khairul; Firdaus, Muhammad Bambang; Fitri, Triyani Arita; Lusiana; Agustin, Wirta; Agustin
The Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): 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.v11i1.3027

Abstract

The simultaneous regional head elections were over, but during the campaign until it was decided to become regional head there were many comments, both pro and contra. The city of Medan is one of the regions that will hold the 2020 ELECTION during the pandemic. The Medan City Election has decided that the pair Bobby Nasution and Aulia Rachman have won. This victory certainly gets a variety of comments on social media, especially Twitter. This study conducts sentiment analysis to see the sentiment that occurs, namely seeing negative, positive, or neutral comments. This sentiment analysis uses two methods to see the resulting accuracy, namely Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC). This study also looks at the interactions that occur using Social Network Analysis (SNA). In addition to sentiment analysis and SNA, this study also looks at the existence of BOT accounts used in the #PilkadaMedan. The results obtained from the sentiment analysis show that NBC has the highest accuracy, which is 81, 72% with a data proportion of 90:10. Then on SNA, the @YanHarahap account got the highest nodes, namely 911 nodes. Then from 10326 tweets, 11% were suspected of being BOT by the DroneEmprit Academic system.
Penerapan Aplikasi E-Commerce Bagi Usaha Baru Ibu Keripik Nenas Desa Kualu Nenas Rio, Unang; Agustin, Wirta; Bakaruddin, Bakaruddin; Muzawi, Rometdo
Jurnal Pengabdian UntukMu NegeRI Vol. 5 No. 2 (2021): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v5i2.3141

Abstract

Abstrak: Kabupaten Kampar merupakan bagian wilayah Provinsi Riau yang memiliki potensi untuk pengembangan komoditas nenas. Sentra produksi tanaman nenas Kabupaten Kampar berada di Kecamatan Tambang yaitu di Desa Kualu Nenas dan Desa Rimbo Panjang dengan rata-rata produksi masing-masingnya sebesar 875 ton/hektar dan 1,6 ton/hektar (Dinas Pertanian Kabupaten Kampar, 2012). Keripik nenas merupakan produk olahan yang paling banyak dikembangkan oleh pengrajin keripik nenas di Desa Kualu Nenas. Agroindustri keripik nenas di Kabupaten Kampar sebenarnya menghadapi berbagai permasalahan, seperti juga yang dialami oleh Pengusaha Keripik “Usaha Baru Ibu”. Permasalahan yang dihadapi pengusaha keripik nenas “Usaha Baru Ibu” belum adanya pemanfaatan teknologi informasi untuk promosi sehingga pemasaran produk usaha keripik dari “Usaha Baru Ibu” belum bisa menjangkau masyarakat luas, oleh sebab itu hal ini sangat berpengaruh terhadap perolehan pendapatan yang kurang optimal dan tidak sesuai dengan yang diinginkan, Untuk pemasarannya dominan dilakukan di sekitar Kampar dan pekanbaru. Melalui program kemitraan masyarakat ini, solusi yang ditawarkan adalah dengan membangun dan menerapkan aplikasi e-commerce baik dalam bentuk website ataupun mobile commerce penjualan Keripik Nenas Kabupaten Kampar dalam meningkatkan pendapatan “Usaha Baru Ibu”. Metode pelaksanaan dalam Program Kemitraan Masyarakat yang digunakan adalah : 1. Focus Group Discussion, 2. Partisipatory Research Action 3. Metode pelatihan dan pendampingan.
Co-Authors -, Dwi Haryono -, T. Sy. Eiva Fadtha -, Tashid Adlan, Radhi Agustin Agustin Ahmad Zamsuri, Ahmad Anam, M Khairul Arita Fitri, Triyani Azhari, Zahra Bakaruddin Bakaruddin bambang suyono Edwar Ali Efrizoni, Lusiana Firdaus, Muhammad Bambang Fransiskus Zoromi Fransiskus Zoromi Hazira, Nadila Helda Yenni, Helda Hendrawan, Riki Irwanda Syahputra Junadhi Junadhi Karpen Kurniawan, Fadly Lathifah, Lathifah Lusiana Lusiana Efrizoni Lusiana Lusiana Lusiana Lusiana Mahendra, Muhammad Ihza Muhammad Bambang Firdaus Muhammad Oase Ansharullah Muhammad Oase Ansharullah Munawir Munawir Nora Lizarti Nurjayadi Nurjayadi Nurul fadillah, Nurul Pandu Pratama Putra Pratama, Farhan Putra, Ryanda Satria Radhi Adlan Rahmaddeni Rahmaddeni Rahmiati Rahmiati Rini Yanti Rini Yanti, Rini Rizky Afrinanda Rometdo Muzawi, Rometdo Sari, Atalya Kurnia Septia, Rapindra Suhada, Khairus Susi Erlinda SUSI ERLINDA Syahrul Imardi Syam, Salmaini Safitri Tashid, Tashid Torkis Nasution Tri Putri Lestari, Tri Putri Triyani Arita Fitri Unang Rio Unang Rio, Unang Rio Yaakub, Saleh Yanti, Rini Yoyon Efendi Yulya Muharmi Zahra Azhari