Article Per Year (5 Year)
p-Index From 2021 - 2026
1.037
P-Index
This Author published in this journals
All Journal Indonesian Journal of Artificial Intelligence and Data Mining SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan QARDHUL HASAN: MEDIA PENGABDIAN KEPADA MASYARAKAT Teknika BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer
Mustika Ulina
Universitas Mikroskil
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
INTRODUCTION OF DEVICES AND ASSEMBLING COMPUTERS TO STUDENTS Apriyanto Halim; Mustika Ulina; Joosten Joosten
Qardhul Hasan: Media Pengabdian kepada Masyarakat Vol. 9 No. 2 (2023): AGUSTUS
Publisher : Universitas Djuanda Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30997/qh.v9i2.8148

Abstract

SMK Swasta Methodist Tanjung Morawa is a private school under the auspices of the Kasih Imanuel Indonesia Methodist Foundation, which was established in 2008. SMK Swasta Methodist Tanjung Morawa has various majors, one of which is Network and Computer Engineering (TKJ). Assembling computer equipment is one of the subjects that can help students become more familiar with computer equipment and know how to fix it and of course this lesson is already a lesson that is in accordance with the majors of SMK Swasta Methodist Tanjung Morawa students, namely Computer Network Engineering (TKJ). The students have studied theoretically related to assembling computer equipment, therefore, the Faculty of Informatics, Universitas Mikroskil offers activities in the form of training in assembling computer equipment to improve students' ability to have good knowledge in computer equipment and how to assemble it. This training activity lasted for 2 days and was carried out in the accounting laboratory at Universitas Mikroskil. During this training activity the students were given pre-test questions, materials and case studies, post-tests and final feedback.
Optimizing Deep Neural Networks Using ANOVA for Web Phishing Detection Wulan Sri Lestari; Mustika Ulina
Teknika Vol. 13 No. 1 (2024): Maret 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i1.758

Abstract

Phishing attacks are crimes committed by sending spoofed Web URLs that appear to come from a legitimate organization in order to obtain another party's sensitive information, such as usernames, passwords, and other confidential data. The stolen information is then used to commit fraud, such as identity theft and financial fraud, and can cause reputational damage to the party that is the victim of the phishing attack. This can cause great harm to the victimized individual or organization. To overcome these problems, this research uses feature selection using ANOVA and Deep Neural Networks (DNN) to detect web phishing attacks. Feature selection is used to optimize the performance of the DNN model to achieve more accurate results. Based on the results of feature selection using ANOVA, there are 52 attributes that have a significant impact on web phishing attack detection. The next step is to implement DNN to build a web phishing attack detection model. The results of testing the web phishing detection model show that in the training phase, the accuracy value increased by 17.51% for the 80:20 dataset and 18.39% for the 70:30 dataset. During the testing phase, the accuracy value increased by 17.8% for the 80:20 dataset and 18.58% for the 70:30 dataset. The resulting recognition model shows consistent and reliable results not only during training, but also during testing in situations closer to real-world conditions. Conclusively, the use of ANOVA proves effective in mitigating less relevant features and contributing to the optimization of web phishing detection models.
Pengembangan sistem informasi berbasis web pada pesantren Tahfizh Daarul Mafaza Wulan Sri Lestari; Mustika Ulina
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 8, No 2 (2024): June
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v8i2.23032

Abstract

AbstrakPromosi merupakan bagian yang sangat penting bagi sebuah sekolah atau pesantren untuk membantu memperkenalkan lembaga pendidikan mereka kepada masyarakat dan dapat meningkatkan jumlah calon siswa/santri yang mendaftar. Pesantren Tahfizh Daarul Mafaza merupakan sebuah pesantren yang memiliki visi melahirkan generasi islami yang hafal al-quran 30 juz, berakhlakul karimah, mandiri, dan siap menghadapi tantangan zaman. Saat ini, untuk mendukung kegiatan promosi, pesantren hanya menggunakan brosur dan fanpage Facebook. Namun brosur memiliki beberapa kekurangan seperti terbatasnya jangkauan promosi, biaya produksi brosur yang tinggi, tidak interaktif dan tidak dapat diperbaharui dengan cepat jika terjadi perubahan informasi. Untuk mengatasi masalah tersebut, maka kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan mengembangkan website yang berisi informasi tentang pesantren sebagai media promosi. Pengembangan webiste ini dilakukan dengan tahapan analisis kebutuhan mitra, perancangan tampilan, pembuatan sistem, dan pengujian sistem. Diakhir kegiatan setelah website selesai, tim PkM memberikan pelatihan kepada mitra untuk penggunaan dan pengelolaan website serta penyebaran kuesioner. Berdasarkan hasil feedback dan kuesioner yang diberikan, menunjukkan bahwa pihak pesantren senang terhadap website yang dibangun dan berharap ke depannya dapat memberikan pengaruh yang signifikan dalam pengembangan pesantren. Kata kunci: sistem informasi; pesantren; promosi AbstractPromotion plays a crucial role in showcasing educational institutions, such as schools or boarding schools, to the public, ultimately boosting enrollment of prospective students. Pesantren Tahfizh Daarul Mafaza is dedicated to fostering an Islamic generation capable of memorizing the Quran's 30 juz, possessing exemplary character, independence, and readiness to confront contemporary challenges. Presently, the pesantren relies solely on brochures and a Facebook fan page for promotional efforts. However, brochures have several disadvantages, including limited advertising reach, high production costs, lack of interactivity, and the inability to quickly update information as it changes. To overcome this problem, this Community Service (PkM) activity aims to develop a website that contains information about pesantren as a promotional media. The website development process involved analyzing the partners' needs, designing interfaces, system development, and rigorous testing. Upon completion of the website, the PkM team conducted training sessions for partners on website usage and management, followed by the distribution of questionnaires. The feedback received from the training and questionnaires indicates the pesantren's high satisfaction with the developed website, expressing hopes that it will significantly contribute to the pesantren's future development. Keywords: information system; pesantren; promotion.
Forex Price Predictions using Hybrid TCN-LSTM and LSTM-TCN Models Caroline Caroline; Wulan Sri Lestari; Mustika Ulina
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 8 No. 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Forecasting financial market prices, particularly foreign exchange (forex) rates, remains a substantial difficulty due to the market's inherent unpredictability, intricacy, and turbulent characteristics. By combining the Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) models into a hybrid framework, this study overcomes this difficulty and improves prediction accuracy.  The MinMaxScaler function was used to standardize the input data prior to training, bringing all values into a range between 0 and 1.  An 80% training segment and a 20% testing segment were then separated from the prepared dataset.  We tested two different hybrid architectures, the LSTM-TCN and the TCN-LSTM, with the EUR/USD, AUD/USD, and GBP/USD value pairs.  With uniform parameters applied to both models during training, the Root Mean Squared Error (RMSE) measure was used for all performance evaluations in order to ensure a fair comparison and determine which model was better. The LSTM-TCN architecture proved to be the superior predictor on the testing set. It recorded a lower average RMSE of 0.003911. This result contrasts with the TCN-LSTM model's performance, which yielded a higher average RMSE of 0.004181.
Klasifikasi Perilaku Keuangan UMKM Medan dengan Machine Learning dan SHAP Saliman; Rivaldi Lubis; Sunaryo Winardi; Mustika Ulina
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 2 (2026): September (In Progress)
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i2.224

Abstract

Good financial behavior is an important factor in sustaining micro, small, and medium enterprises (MSMEs), particularly in financial recording, debt management, and investment planning. Previous research using Structural Equation Modeling Partial Least Squares (SEM-PLS) identified financial attitude as the dominant factor influencing MSMEs’ financial behavior in Medan City. Based on these findings, this study develops a complementary machine learning-based approach to classify MSMEs’ financial behavior at the individual level and evaluate its consistency with SEM results through SHAP-based Explainable Artificial Intelligence (XAI). The dataset consists of 100 MSME respondents with seven main features, including three financial constructs and four demographic variables. Three ensemble algorithms, namely CatBoost, XGBoost, and Random Forest, were evaluated using hold-out and Stratified 5-Fold Cross-Validation. The results show that CatBoost achieved the best performance with 80.00% accuracy and 82.46% F1-score. SHAP analysis confirmed the dominance of attitude score and revealed the significant predictive contribution of demographic variables. Integrating machine learning and SHAP is an effective complementary approach to extend the understanding of MSMEs’ financial behavior comprehensively.
Co-Authors Apriyanto Halim Caroline Caroline Joosten Joosten Rivaldi Lubis Saliman Winardi, Sunaryo Wulan Sri Lestari