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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JURIKOM (Jurnal Riset Komputer) JSI (Jurnal sistem Informasi) Universitas Suryadarma Indonesian Journal of Education and Mathematical Science IT (INFORMATIC TECHNIQUE) JOURNAL Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Blend Sains Jurnal Teknik Jutech: Jurnal Teknologi Informasi Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer DEVOTE: Jurnal Pengabdian Masyarakat Global Jurnal Komprehenshif Hanif Journal of Information Systems Portal Riset dan Inovasi Sistem Perangkat Lunak Al'Adzkiya International of Computer Science and Information Technology Journal Tsabit Inventa: Journal of Science, Technology, and Innovation
Mulkan Azhari
Universitas Muhammadiyah Sumatera Utara
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Komparasi Algoritma KNN Dan SVM Untuk Klasifikasi Kesehatan Mental Pada Usia Remaja Rizky Hidayat Hasibuan; Mulkan Azhari
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 10, No 1 (2026): April 2026
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v10i1.28151

Abstract

Mental health among adolescents is a critical issue that continues to increase and is often not detected early due to the limitations of assessment methods that remain largely subjective. This study aims to classify adolescent mental health levels based on stress levels using a machine learning approach and to compare the performance of the KNN and SVM algorithms. The dataset used consists of 1,100 adolescent records obtained from GitHub, comprising 11 predictor attributes and one target attribute, namely stress_level, which is classified into three categories: low, moderate, and high. The research stages include data preprocessing, EDA, feature selection, handling class imbalance using the SMOTE, modeling, and evaluation. Model testing was conducted using several training–testing split ratios. Model performance was evaluated using confusion matrix. The results indicate that the SVM algorithm achieved the best performance with an accuracy of 89.55% and an F1-Score of 89.58% using an 80:20 data split prior to the application of SMOTE. Overall, SVM demonstrated higher stability and accuracy compared to KNN in classifying adolescent mental health levels, indicating its strong potential as a data-driven early detection tool for adolescent mental health issues. Keywords: Adolescent Mental Health, Stress Level Classification, K-Nearest Neighbor, Support Vector Machine
Analisis Perbandingan Metode LSTM dan BiLSTM untuk Prediksi Harga Saham Menggunakan Alpha Vantage Rifqi Yafik; Mulkan Azhari
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.650

Abstract

Pasar saham Indonesia mengalami pertumbuhan signifikan, namun fluktuasi harga yang tinggi tetap menjadi tantangan utama bagi investor. Penelitian ini bertujuan membandingkan performa dua model deep learning, Long Short-Term Memory (LSTM) dan Bidirectional LSTM (BiLSTM), untuk memprediksi harga penutupan harian dua saham blue chip, PT Bank Central Asia Tbk (BBCA) dan PT Telkom Indonesia Tbk (TLKM). Data historis dari Januari 2019 hingga Desember 2023 diperoleh melalui Alpha Vantage API. Proses penelitian mencakup normalisasi data dengan MinMaxScaler dan pembentukan sliding window untuk pemodelan deret waktu. Model LSTM dan BiLSTM dilatih menggunakan TensorFlow-Keras, dengan optimasi hyperparameter melalui metode Grid Search yang menguji kombinasi units, batch size, epochs, dan dropout rate. Hasil eksperimen menunjukkan bahwa model BiLSTM memberikan akurasi prediksi yang lebih tinggi dibandingkan LSTM pada kedua saham. Untuk saham BBCA, BiLSTM mencatat RMSE sebesar 0.0178, lebih baik dari LSTM yang mencatat RMSE 0.0180. Begitu pula pada saham TLKM, BiLSTM mencapai RMSE 0.0172, mengungguli LSTM dengan RMSE 0.0199. Keunggulan BiLSTM disebabkan kemampuannya memproses data secara dua arah, yang memungkinkan model menangkap pola kontekstual dan titik pembalikan tren dengan lebih baik. Penelitian ini berkontribusi pada pengembangan model prediksi saham yang lebih akurat dan sistematis bagi peneliti dan praktisi di pasar modal.
Co-Authors Al Khowarizmi Andi Zulherry Anggy Wulandari Efendi, Syahril Ferdy Riza Halim Maulana Husaini, Abdillah Ichsan, Aulia ILHAM Indah Purnama Sari Jannah, Aulia Khairani, Ade Irma Kipa Jundapri Manurung, Asrar Aspia Maulana, Eka Syarif Maulia Rahman Nadia Febrina Nasution, Perdinal Oris Krianto Sulaiman Prastia, Ilham Purwanto Purwanto Putra, Aldryan Bhara Rifqi Yafik Rika Rosnelly, Rika Rizky Hidayat Hasibuan Siti Khairun Nisa Situmorang, Zakaria Syafik, Hafizan Zahra Apriyani Hakim Nasution