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Jurnas Nasional Teknologi dan Sistem Informasi
Published by Universitas Andalas
ISSN : 24768812     EISSN : 24603465     DOI : https://dx.doi.org/10.25077/TEKNOSI
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal ini menerbitkan artikel penelitian (research article), artikel telaah/studi literatur (review article/literature review), laporan kasus (case report) dan artikel konsep atau kebijakan (concept/policy article), di semua bidang : Geographical Information System, Enterpise Application, Bussiness Intelligence, Data Warehouse, Network Computer Security, Data Mining, Computer Architecture Design, Mobile Computing, Computing Theory, Embedded system, Decision Support System
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 428 Documents
 39   40   41   42   43 
Systematic Literature Review: Optimasi Model Klasifikasi pada Imbalanced Data Menggunakan SMOTE, Hyperparameter Tuning, dan Ensemble Learning Muhammad Alfin Ghozali; Agus Bahtiar
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 2 (2026): Agustus 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i2.2026.350-362

Abstract

Imbalanced Data merupakan salah satu permasalahan utama dalam pengembangan model klasifikasi karena menyebabkan model cenderung mempelajari kelas mayoritas sehingga kemampuan mendeteksi kelas minoritas menjadi menurun. Berbagai pendekatan, seperti Synthetic Minority Oversampling Technique (SMOTE), Hyperparameter Tuning, dan Ensemble Learning, telah dikembangkan untuk meningkatkan performa model pada kondisi tersebut. Penelitian ini bertujuan menganalisis karakteristik penelitian, mengkaji penerapan SMOTE, Hyperparameter Tuning, dan Ensemble Learning, serta mengidentifikasi Research Gap dalam optimasi model klasifikasi pada Imbalanced Data. Penelitian menggunakan metode Systematic Literature Review (SLR) dengan mengacu pada pedoman PRISMA 2020. Proses kajian meliputi penyusunan Research Question, pencarian literatur pada basis data Scopus, seleksi artikel berdasarkan kriteria inklusi dan eksklusi, Quality Assessment, ekstraksi data, serta sintesis hasil. Sebanyak 14 artikel yang memenuhi kriteria ditetapkan sebagai Primary Studies. Hasil sintesis menunjukkan bahwa SMOTE merupakan teknik yang paling banyak digunakan untuk menangani Imbalanced Data, sedangkan Hyperparameter Tuning berperan dalam memperoleh konfigurasi model yang optimal melalui pendekatan seperti Optuna dan Bayesian Optimization. Selain itu, Ensemble Learning, khususnya Stacking dan Soft Voting, banyak diintegrasikan dengan teknik optimasi lainnya untuk meningkatkan akurasi, stabilitas, dan kemampuan generalisasi model. Penelitian ini menyimpulkan bahwa integrasi SMOTE, Hyperparameter Tuning, dan Ensemble Learning menjadi pendekatan yang dominan dalam optimasi model klasifikasi pada Imbalanced Data. Penelitian juga mengidentifikasi peluang pengembangan pada aspek interpretabilitas model, efisiensi komputasi, serta pengembangan kerangka optimasi yang lebih terintegrasi.
Development of a Real-time Solar Panel Power Prediction Model Using the Long Short-term Memory Christio Revano Mege; Ferizandi Qauzar Gani; Amrina Mustaqim; Listra Yehezkiel Ginting; Hesti Wahyu Handani; Jodes Parasian Simatupang; Friska Hasugian
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 2 (2026): Agustus 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i2.2026.363-368

Abstract

Indonesia’s remote islands face significant challenges in electricity access due to the high cost and logistical difficulties of extending the national grid, leading many communities to rely on expensive and polluting diesel generators. Solar-based microgrids offer a sustainable alternative, yet the intermittent nature of solar energy, driven by fluctuating weather conditions, poses major obstacles to a reliable power supply and efficient system sizing. This study addresses these issues by developing a real-time solar panel power prediction model using Long Short-Term Memory (LSTM) networks. A 50 Wp solar panel system equipped with an INA260 current sensor, a voltage sensor, a DHT-22 temperature sensor, and an ESP32 microcontroller was constructed to collect real-time voltage, current, and temperature data at 10-second intervals. The collected data underwent preprocessing, feature engineering, and transformation into supervised learning sequences for training. Three temporal resolutions of the LSTM model were systematically evaluated: 3-minute, 2-minute, and 1-minute, all with 30 output timesteps. Performance was assessed using rolling-window predictions on the held-out test set with metrics including RMSE, MAPE, and R². Results demonstrated that finer temporal resolution significantly improves forecasting accuracy. The 1-minute variation achieved the best performance with the lowest RMSE and highest R², effectively capturing both diurnal patterns and short-term fluctuations in solar power output. The developed LSTM model enables accurate short-term predictions (30–90 minutes ahead), supporting proactive energy management, including optimized battery charging, load scheduling, and reduced grid dependency. Future work will incorporate additional meteorological variables and seasonal data to improve model robustness further. Three temporal variations of the LSTM model were systematically evaluated: 3-minute, 2-minute, and 1-minute resolutions, all with 30 output timesteps. Performance was assessed using rolling-window predictions on the held-out test set with metrics including RMSE, MAPE, and R². Results demonstrated that finer temporal resolution significantly improves forecasting accuracy. The 1-minute variation achieved the best performance with the lowest RMSE and highest R², effectively capturing both diurnal patterns and short-term fluctuations in solar power output. The developed LSTM model enables accurate short-term predictions (30–90 minutes ahead), supporting proactive energy management such as optimized battery charging, load scheduling, and reduced grid dependency. Future work will incorporate additional meteorological variables and seasonal data to further improve model robustness.
Implementasi Transfer Learning Menggunakan DenseNet121 untuk Deteksi Presentation Attack pada Citra Wajah Septian Nuno Zildjian; Dian Nurdiana; Fonda Leviany
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 2 (2026): Agustus 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i2.2026.266-275

Abstract

Remote examinations have become a cornerstone of distance education in Indonesia, particularly in large-scale institutions such as Universitas Terbuka and Massive Open Online Course (MOOC) platforms. However, the reliability of face verification in commercial online proctoring systems continues to be challenged by presentation attacks (face spoofing). Using simple and inexpensive media such as printed photographs, images displayed on another device, masks, or mannequins, impersonators can deceive the initial face verification process without requiring advanced technical skills. Once authenticated, they are able to complete the entire examination without being detected. Given the large number of participants in distance education examinations, manual verification of every examinee is impractical. Therefore, an automated, fast, lightweight, and reliable detection solution is needed to efficiently screen webcam captures at scale within university server infrastructures. This study addresses this challenge by developing a presentation attack detection model based on transfer learning using the DenseNet121 architecture. The model was trained on a multiclass facial image dataset consisting of 1,403 images across six categories: fake_mannequin, fake_mask, fake_printed, fake_screen, fake_unknown, and realperson. Partial fine-tuning was applied to the final convolutional layers (denseblock4 and norm5), while the training process employed the AdamW optimizer, the ReduceLROnPlateau learning rate scheduler, and early stopping to enhance performance and prevent overfitting. Experimental results achieved an accuracy of 87.68%, a weighted precision of 88.79%, a weighted recall of 87.68%, and a weighted F1-score of 87.62%, demonstrating stable and consistent performance across all attack categories. The best-performing model was subsequently deployed in a Streamlit-based application to provide an interactive and user-friendly demonstration for non-technical users. These findings demonstrate that the proposed DenseNet121 transfer learning approach is accurate, lightweight, and computationally efficient, making it a promising additional security layer for online examination proctoring systems in large-scale distance education environments amid the growing threat of face spoofing attacks.
Pemetaan Kualitas Udara Menggunakan Integrasi K-Means dan Random Forest Berbasis Data Sentinel-5P Muhammad Ridwan; Fenty Kurnia Oktorina; Hanifah Khairiah; Fajril Akbar
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 2 (2026): Agustus 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i2.2026.222-230

Abstract

Kualitas udara merupakan salah satu indikator penting dalam pengelolaan lingkungan, namun ketersediaan data hasil pengukuran lapangan masih terbatas pada banyak wilayah, termasuk Kabupaten Kampar. Penelitian ini bertujuan menganalisis sebaran spasial polutan atmosfer, memetakan kualitas udara menggunakan integrasi metode K-Means dan Random Forest, serta mengidentifikasi kontribusi masing-masing parameter polutan terhadap pembentukan kelas kualitas udara. Penelitian memanfaatkan data Sentinel-5P Level-3 periode Januari–Desember 2025 yang diolah melalui Google Earth Engine (GEE). Lima parameter atmosfer yang digunakan meliputi nitrogen dioksida (NO2), karbon monoksida (CO), sulfur dioksida (SO2), ozon (O3), dan Aerosol Index (AI). Metode K-Means digunakan untuk membentuk pseudo-label melalui proses clustering, kemudian hasilnya digunakan sebagai data pelatihan pada algoritma Random Forest untuk menghasilkan peta kualitas udara. Hasil penelitian menunjukkan bahwa kualitas udara di Kabupaten Kampar terbagi ke dalam tiga kelas, yaitu kelas rendah sebesar 45 persen, kelas sedang 44 persen, dan kelas tinggi 11 persen. Wilayah dengan kualitas udara tinggi terkonsentrasi di bagian selatan Kabupaten Kampar, terutama Kecamatan Kampar Kiri Hulu dan sekitarnya. Analisis feature importance menunjukkan bahwa CO dan Aerosol Index merupakan parameter yang memberikan kontribusi terbesar terhadap pembentukan kelas kualitas udara, diikuti oleh O3, NO2, dan SO2. Selain itu, distribusi spasial kelas kualitas udara tinggi menunjukkan kesesuaian dengan beberapa lokasi kejadian kebakaran hutan dan lahan pada tahun 2025. Penelitian ini menunjukkan bahwa integrasi K-Means dan Random Forest berbasis data Sentinel-5P berpotensi menjadi alternatif pemetaan kualitas udara pada wilayah yang memiliki keterbatasan stasiun.
High Confidence, Low Accuracy: Probing Ownership Bias in LLMs on Banjarese and Dayak Ngaju Muhammad Ihsan; Didi Andriawan
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 2 (2026): Agustus 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i2.2026.386-392

Abstract

AI assistants often give wrong answers with high confidence, especially when answering in regional languages. One cause is ownership bias: a model trusts its own answer more when that answer appears in the assistant role. This effect is well documented in English, but it is unclear whether it also appears in low-resource languages such as Banjarese and Dayak Ngaju. This study measures the extent of ownership bias and the calibration quality of four open-weight 8-bit LLMs when tested on school exam questions in Banjarese and Dayak Ngaju, drawn from the IndoMMLU benchmark. Four models (Qwen3-8B, Llama-3.1-8B, SahabatAI-Llama-8B, and SahabatAI-Gemma2-9B) are evaluated under two conditions: Own, in which the model’s answer is placed in the assistant role, and Reframed, in which the same answer is attributed to the user. We report accuracy, ownership bias, Expected Calibration Error (ECE), and Brier Score. Llama-3.1-8B shows the highest ownership bias (49.93 pp on Banjarese and 53.83 pp on Dayak Ngaju), while SahabatAI-Gemma2-9B shows almost no bias (0.91–1.33 pp) but assigns 100% confidence to every answer. The same pattern appears in both languages, indicating that ownership bias is driven by the model's architecture and training, not by the language being tested. AI assistants that are overconfident in English remain overconfident in Banjarese and Dayak Ngaju as well. Practitioners should treat verbalized confidence as an unreliable signal and use model-level calibration such as temperature scaling or logprob-based methods, rather than relying on prompt-level reframing alone.
Aplikasi Alarm Cerdas Berbasis MobileNetV2 TensorFlow Lite Verifikasi Kesadaran Pengguna Muhammad Zakfar Sodik; Renhat Denil Ramadhan; Andika Sandi Pranata; Zidan Alghifari; Dani Rofianto; Khusnatul Amaliah
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 2 (2026): Agustus 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i2.2026.411-421

Abstract

Rendahnya kemampuan alarm konvensional dalam memastikan pengguna benar-benar terbangun masih menjadi penyebab keterlambatan bangun pada sebagian pelajar, mahasiswa maupun pekerja. Sebagian besar aplikasi alarm konvensional hanya mengandalkan keluaran berupa suara atau getaran tanpa mekanisme untuk memastikan bahwa pengguna benar-benar telah terbangun. Penelitian ini bertujuan merancang aplikasi WakeUp AI, yaitu aplikasi alarm cerdas berbasis Android yang memanfaatkan kecerdasan buatan untuk memverifikasi kondisi pengguna sebelum alarm dapat dihentikan. Sistem menerapkan metode Transfer Learning dengan arsitektur MobileNetV2 untuk mengklasifikasikan kondisi mata terbuka dan mata tertutup. Sebelum proses klasifikasi dilakukan, citra dipraproses menggunakan teknik Eye Region Cropping sehingga model hanya memproses area mata. Selanjutnya model dijalankan secara lokal menggunakan TensorFlow Lite agar dapat bekerja tanpa koneksi internet. Untuk meningkatkan kestabilan hasil deteksi diterapkan algoritma Rolling Average dan Leaky Bucket Accumulator, kemudian proses verifikasi dilanjutkan dengan mengimplementasikan tantangan Random Question Challenge sederhana sebagai verifikasi kognitif pengguna. Berdasarkan implementasi dan pengujian yang telah dilakukan,  sistem mampu menghasilkan aplikasi alarm yang ringan, cepat, serta lebih efektif dibandingkan alarm konvensional dalam memastikan pengguna benar-benar berada pada kondisi sadar sebelum alarm dinonaktifkan.
Perancangan Platform Securities Crowdfunding Syariah Terintegrasi dengan Ekosistem Bank Digital Berbasis Rapid Application Development Raden Bestand; Nur Hidayah; Silva Amelia; Nabila Qalby; Rifan Wijaya
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 2 (2026): Agustus 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i2.2026.402-410

Abstract

Micro, Small, and Medium Enterprises (MSMEs) serve as the backbone of the Indonesian economy, yet they continue to face a funding gap due to collateral constraints at conventional banking institutions. Concurrently, the rise of Islamic digital banking offers massive opportunities for financial inclusion. However, current platforms suffer from an "app-switching" problem, where banks rely heavily on third-party integrations for investment instruments rather than providing in-house, real-sector funding solutions. This study aims to address this gap by designing "Jago Modal," a mobile-based Securities Crowdfunding (SCF) system proposed as an integrated platform within Bank Jago Syariah. This research utilizes the Rapid Application Development (RAD) approach, translating Islamic financial rules and national regulations into strict computational business rules. The system architecture models the integration of core Sharia contracts—specifically Mudharabah (profit-sharing), Musyarakah (equity partnership), and Wadiah Yad Dhamanah (safekeeping)—as foundational functional requirements underlying the Unified Modeling Language (UML) and Business Process Model and Notation (BPMN) designs. The result of this research is an application prototype capable of mitigating the legal and operational risks of standalone SCF platforms, delivering a Sharia-compliant capital solution for MSMEs, and simultaneously empowering digital bank customers with seamless, direct investment opportunities.
Pembangunan Sistem Pendukung Keputusan untuk Identifikasi Risiko Perundungan dan Rekomendasi Tindak Lanjut pada Siswa Menggunakan Metode Absolute Analytic Hierarchy Process Ricky Akbar; Hafizah Hanim; Mikail Samyth Habibillah; Ihsan Auliya Habibirrohim; Sheva Ramadhan; Duha Alul Bariq
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 2 (2026): Agustus 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i2.2026.393-401

Abstract

Perundungan di lingkungan sekolah merupakan permasalahan serius yang berdampak pada kondisi psikologis dan prestasi akademik siswa. Namun selama ini mekanisme identifikasi risikonya di kebanyakan sekolah masih bersifat konvensional dan subjektif, belum menggunakan metode atau sistem yang terstruktur dan terkomputerisasi. Sebagai contoh proses identifikasi di beberapa sekolah di Kota Padang saat ini masih mengandalkan laporan dari siswa atau orang tua, berdasarkan hasil observasi guru atau wali kelas di sekolah, dan belum mengintegrasikan semua sumber informasi tersebut. Maka salah satu solusi yang dapat dilakukan adalah dengan membangun Sistem Pendukung Keputusan (SPK) berbasis web menggunakan metode Analytical Hierarchy Process (AHP) model Absolute, yang berfungsi penuh untuk membantu dengan cepat dan tepat proses identifikasi tingkat risiko perundungan terhadap siswa dan memberikan rekomendasi tindak lanjut pada sekolah. Metode Absolute AHP ini dipilih karena mampu mengevaluasi banyak alternatif secara independen menggunakan sub-kriteria sebagai standar penilaian baku, tanpa memerlukan perbandingan berpasangan antar alternatif sebagaimana pada AHP konvensional. Sistem ini menggunakan lima kriteria penilaian, yaitu Sosiometri, Media Sosial, Absensi, Laporan Guru, dan Keaktifan Ekstrakurikuler, serta lima sub-kriteria skala intensitas risiko mulai dari Sangat Parah hingga Sangat Aman. Bobot kriteria dan sub-kriteria diperoleh melalui perbandingan berpasangan berbasis expert judgment Guru BK, dengan validasi menggunakan uji Consistency Ratio (CR) yang menunjukkan nilai di bawah 10%. Pengujian terhadap 10 sampel siswa sebagai alternatif menghasilkan identifikasi peringkat risiko beserta rekomendasi tindak lanjutnya. Sistem ini terbukti menghasilkan penilaian yang objektif, terstandarisasi, dan efisien sehingga mendukung Guru BK dalam mengambil keputusan intervensi yang lebih tepat sasaran dan berbasis data.

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