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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics Jurnal Informatika dan Teknik Elektro Terapan JOIN (Jurnal Online Informatika) BAREKENG: Jurnal Ilmu Matematika dan Terapan Building of Informatics, Technology and Science Abdi Dosen : Jurnal Pengabdian Pada Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Brahmana : Jurnal Penerapan Kecerdasan Buatan Journal La Multiapp Action Research Literate (ARL) Malcom: Indonesian Journal of Machine Learning and Computer Science Cerdika: Jurnal Ilmiah Indonesia The Indonesian Journal of Computer Science
Zahra, Amalia
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Religion Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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PREDIKSI KELULUSAN SISWA SEKOLAH MENENGAH PERTAMA MENGGUNAKAN MACHINE LEARNING Naibaho, Agusti Frananda Alfonsus; Zahra, Amalia
Jurnal Informatika dan Teknik Elektro Terapan Vol. 11 No. 3 (2023)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v11i3.3056

Abstract

In recent years, there has been a number of students who graduated late at Lubuk Alung 1st State Junior Highschool. This statement is supported by graduation data from Lubuk Alung 1st Satet Junior Highschool. Therefore, it is necessary to predict students’ graduation status to identify which factors influence the student’s graduation, which will also consequently help the school to solve problem more easily. To solve this problem, the researchers predict student graduation based on student graduation information. The attributes used are personal data related to students, student academic data, and data related to the work of the student’s parents. This research retrieved data on student graduation from schools that have been recapitulated. The classification algorithms used to predict students’ graduation are decision tree, random forest, and extreme gradient boosting with grid searchCV and k-fold=5. The prediction accuracy using the random forest algorithm outperforms the others with a value of 99.5%.
HUBUNGAN ANTARA CITRA TUBUH DENGAN PERILAKU DIET MAHASISWI DI AKADEMI KEPERAWATAN AL HIKMAH 2 BREBES Karyawati, Tati; Seventina, Healthy; Zahra, Amalia
Cerdika: Jurnal Ilmiah Indonesia Vol. 3 No. 09 (2023): Cerdika : Jurnal Ilmiah Indonesia
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/cerdika.v3i09.667

Abstract

Tujuan dalam penelitian ini untuk mengetahui hubungan antara citra tubuh dengan perilaku diet mahasiswa di Akademi Keperawatan Al Hikmah 2 Brebes. Penelitian ini menggunakan metode kuantitatif dengan desain cross sectional. Penelitian ini dilakukan di Akper Al Hikmah 2 Brebes untuk menganalisis hubungan antara citra tubuh dan perilaku diet pada mahasiswa. Responden sebanyak 59 mahasiswa mengisi kuesioner terkait citra tubuh dan perilaku diet mereka. Hasil penelitian menunjukkan bahwa 50,8% responden memiliki citra tubuh negatif, sedangkan 49,2% memiliki citra tubuh positif. Sebanyak 50,8% responden memiliki perilaku diet sehat, dan 49,2% memiliki perilaku diet tidak sehat. Analisis statistik menggunakan uji chi-square menghasilkan p-value sebesar 0,006, yang lebih kecil dari tingkat signifikansi ? = 0,05. Hal ini mengindikasikan bahwa terdapat hubungan yang signifikan antara citra tubuh dan perilaku diet mahasiswa di Akper Al Hikmah 2 Brebes, sehingga dapat disimpulkan bahwa citra tubuh berpengaruh terhadap perilaku diet mahasiswa.
Javanese and Sundanese speech recognition using Whisper Raharjo, Alim; Zahra, Amalia
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p253-261

Abstract

Automatic speech recognition (ASR) technology is essential for advancing human-computer interaction, particularly in a linguistically diverse country like Indonesia, where approximately 700 native languages are spoken, including widely used languages like Javanese and Sundanese. This study leverages the pre-trained Whisper Small model an end‑to‑end transformer pretrained on 680,000 hours of multilingual speech, fine tuning it specifically to improve ASR performance for these low resource languages. The primary goal is to increase transcription accuracy and reliability for Javanese and Sundanese, which have historically had limited ASR resources. Approximately 100 hours of speech from OpenSLR were selected, covering both reading and conversational prompts, the data exhibited dialectal variation, ambient noise, and incomplete demographic metadata, necessitating normalization and fixed‑length padding. with model evaluation based on the word error rate (WER) metric. Unlike approaches that combine separate acoustic encoders with external language models, Whisper unified architecture streamlines adaptation for low‑resource settings. Evaluated on held‑out test sets, the fine‑tuned models achieved Word Error Rates of 14.97% for Javanese and 2.03% for Sundanese, substantially outperforming baseline systems. These results demonstrate Whisper effectiveness in low‑resource ASR and highlight its potential to enhance transcription accuracy, support language preservation, and broaden digital access for underrepresented speech communities. 
Deep learning-based spam detection for WhatsApp chatbot fallback reduction Sadewo, Satrio; Zahra, Amalia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp909-918

Abstract

Chatbots on WhatsApp are widely used for customer service, but their effectiveness is often undermined by fallback responses when user input cannot be understood. A major cause of these fallbacks is unsolicited spam, which disrupts interactions and reduces service quality. This study develops and evaluates a spam detection system aimed at reducing fallback rates and enhancing user experience. A comparative analysis was conducted between traditional machine learning models (support vector machine (SVM) and decision tree (DT)) and advanced deep learning architectures, including long short-term memory (LSTM) variants (vanilla, bidirectional, stacked, convolutional neural network (CNN)-LSTM, and encoder-decoder) and transformer-based models (bidirectional encoder representations from transformers (BERT)-base, DistilBERT, and cross-lingual language model robustly optimized BERT pretraining approach (XLM-ROBERTa)). Using 170,000 messages sampled from 18 million interactions collected between July 2022 and December 2023, the models were assessed with standard evaluation metrics. Results show that CNN-LSTM and DistilBERT achieved the most robust performance. CNN-LSTM attained a precision of 0.92, recall of 0.91, F1-score of 0.91, and accuracy of 0.94, while DistilBERT achieved precision of 0.92, recall of 0.89, F1-score of 0.90, and accuracy of 0.93. These findings highlight their superior ability to capture contextual patterns in spam messages. Implementing such models is expected to significantly lower fallback rates, thereby improving chatbot reliability and user satisfaction.
Market-Adaptive Stock Trading through B-WEMA Driven Proximal Policy Optimization Ichsan, Mulia; Zahra, Amalia
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9349

Abstract

Developing automated trading strategies that achieve stable returns while controlling risk remains a central threat in quantitative finance. Many reinforcement learning-based trading systems focus on reward maximization but provide limited justification for the choice of forecasting indicators and often lack comprehensive benchmarking against alternative strategies and risk measures. This essay addresses the problem of integrating a statistically grounded price-smoothing technique with a policy optimization scheme to improve sequential trading decisions under market uncertainty. We propose a hybrid model that combines Brown’s Weighted Exponential Moving Average (B-WEMA) as a trend-sensitive forecasting indicator with a Deep Reinforcement Learning agent trained using Proximal Policy Optimization (PPO). The role of B-WEMA is to provide structured price signals that reduce noise sensitivity, while PPO determines buy and sell actions through policy updates constrained for stable learning. The performance of the proposed model is evaluated over a 10-month trading horizon and compared with a buy-and-hold benchmark and an alternative reinforcement learning method, Advantage Actor-Critic (A2C), both implemented under the same experimental conditions. Empirical results show that the proposed B-WEMA-PPO framework achieved a cumulative return of 23.43% over the test period, outperforming both the benchmark and the A2C-based agent. In addition to cumulative return, risk-adjusted performance metrics, namely volatility and maximum drawdown, are reported to provide a balanced assessment of profitability and risk exposure. These findings suggest that incorporating structured exponential smoothing into policy optimization may enhance the stability and effectiveness of reinforcement learning-based trading strategies.
IMPLEMENTATION OF SCHOOL RELIGIOUS CULTURE IN THE FORMATION OF ELEMENTARY SCHOOL STUDENTS' MORALS: A STUDY AT AL-MUNAWWAR ISLAMIC ELEMENTARY SCHOOL, MANDAILING NATAL Supriani, Ria Rafita; Mustamirrun, Nurizki; Zahra, Amalia; Hasibuan, Reski Wahyuni; Lubis, Tuti Alawiyah; Hafni, Liza; Rohani, Wilda
Abdi Dosen : Jurnal Pengabdian Pada Masyarakat Vol. 10 No. 1 (2026): MARET
Publisher : LPPM Univ. Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/abdidos.v10i1.3278

Abstract

This study aims to analyze the implementation of school religious culture in shaping student morals at Al-Munawwar Integrated Islamic Elementary School in Mandailing Natal. This study used a qualitative approach with a descriptive approach. The research subjects included the principal, homeroom teachers, and students. Data collection techniques included observation, interviews, and documentation. Data analysis was conducted through data reduction, data presentation, and conclusion drawing, using source and technique triangulation to ensure data validity. The results indicate that the implementation of school religious culture is carried out through several activities, namely gate duty, routine religious activities, the habit of congregational prayer, and extracurricular activities. Gate duty plays a role in fostering politeness, respect for teachers, and discipline in students. Routine religious activities accustom students to practicing religious values in their daily lives. The habit of congregational prayer fosters discipline, responsibility, and etiquette in worship. Meanwhile, extracurricular activities serve as a means of strengthening moral values through direct practice, such as cooperation, responsibility, and compliance with rules. The conclusion of this study indicates that the planned and consistent implementation of a school religious culture can shape students' morals gradually and sustainably. School religious culture serves as an effective strategy in character education to shape students with noble morals and religious attitudes.
Co-Authors Al Mukarram, Khasyi Cahyadi, Jonathan Caprisiano, Ebenhaiser Jonathan Chandra, Wiryanata David David Fadhil, Muhammad Farhan Fadzlul Rahman, Ferry Faldi, Faldi Farhan Fadhil, Muhammad Fathur Rahman Hafni, Liza Halim, Rico Harits, Sayekti Hasibuan, Reski Wahyuni Hendra Saputra Henry, Michael Himawan, Randy Ichsan, Mulia Joko Pranoto, Wawan Karyawati, Tati Kristian Handoko Wijaya Sukardjoh Lubis, Tuti Alawiyah Martulandi, Adipta Maskur, Maimunah Michael, Stefanus Muhammad Manaqib, Muhammad Mukhlas, M. Anang Mustamirrun, Nurizki Naibaho, Agusti Frananda Alfonsus Novika, Andien Dwi Raharjo, Alim Ramadhansyah, Muhammad Hafizh Riftiarrasyid, Mohammad Faisal Roesmiatun Purnamadewi, Yasinta Rohani, Wilda Sadewo, Satrio Sams, Andrew Steven Setyoko, Andreas Dimas Seventina, Healthy Suma'inna, Suma'inna Supriani, Ria Rafita Vincent Vincent, Vincent William, Ezra