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Fajril Akbar
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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 18 Documents
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Search results for , issue "Vol 12 No 1 (2026): April 2026" : 18 Documents clear
Pembangunan Aplikasi Mobile Kehadiran Guru Berbasis Geofence, Firebase ID, dan Face Capture Pada SMAN 1 Ulakan Tapakis Muhammad Zaim Milzam; Rahmadoni, Jefril; Dwi Kartika, Afriyanti
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 1 (2026): April 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i1.2026.45-51

Abstract

The teacher attendance process at SMAN 1 Ulakan Tapakis is still carried out manually using paper forms, which are prone to input errors, data loss, and manipulation practices such as leaving absences behind. Efforts to implement technology in the form of fingerprint devices were made, but did not function continuously due to technical constraints. This condition causes the attendance data recapitulation and reporting process to be inefficient and take a long time. Based on these problems, this study aims to build a mobile teacher attendance application based on geofence, Firebase ID, and face capture as a solution to improve data integrity and efficiency in the attendance process at SMAN 1 Ulakan Tapakis. This system was developed using the Waterfall method, through the stages of needs analysis, system design, implementation, and testing. The results of functional testing show that all application features run according to design and can validate teacher attendance automatically based on the location and identity of the user. In addition, the results of efficiency testing show that this system can significantly save time in the attendance and reporting process compared to manual methods. Thus, this application is considered effective in improving the integrity of attendance data and the efficiency of the teacher attendance administration process at SMAN 1 Ulakan Tapakis.
Penerapan Metode TF-IDF Dalam Sistem Informasi Digital Marketing Pada UMKM Bismillah Outlet Hijab Ramadhani, Rani; Suendri, Suendri
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 1 (2026): April 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i1.2026.71-78

Abstract

Pemanfaatan digital marketing menjadi kebutuhan penting bagi UMKM untuk meningkatkan efektivitas promosi dan memperluas jangkauan pasar. Namun, UMKM Bismillah Outlet Hijab masih menghadapi kendala dalam pengelolaan data produk serta penentuan kata kunci promosi yang relevan karena belum tersedianya sistem informasi digital marketing yang terintegrasi. Penelitian ini bertujuan menerapkan metode Term Frequency–Inverse Document Frequency (TF-IDF) pada sistem informasi digital marketing berbasis web untuk menganalisis relevansi kata kunci dan memberikan rekomendasi produk sesuai dengan minat konsumen. Proses pengembangan sistem dilakukan menggunakan model waterfall yang meliputi tahap analisis kebutuhan, perancangan, implementasi, dan pengujian. Sistem dikembangkan menggunakan bahasa pemrograman PHP dengan basis data MySQL, serta memanfaatkan algoritma TF-IDF sebagai metode pembobotan kata pada deskripsi produk. Hasil penelitian menunjukkan bahwa sistem mampu mengelola data produk, melakukan pencarian berbasis kata kunci, serta menampilkan rekomendasi produk berdasarkan nilai bobot TF-IDF tertinggi. Pengujian fungsional menunjukkan seluruh fitur berjalan sesuai kebutuhan, sedangkan uji relevansi menunjukkan metode TF-IDF efektif dalam membantu penentuan kata kunci promosi yang sesuai. Sistem yang dihasilkan diharapkan dapat mendukung pengambilan keputusan pemasaran digital serta menjadi solusi awal transformasi digital bagi UMKM.
Rancang Bangun Sistem Disposisi Surat Berbasis Web untuk Mendukung Transformasi Digital di BPS Kota Banjarmasin Permatasari , Nindy; Rahmi, Mifthahul; Karima, Cahya; Pramawahyudi, Pramawahyudi; Syafaadi, Afian; Nova, Sausan Hidayah; Wibowo, Dwi Agung; Aristi, Nina Mia; Kurnia, Riska; Noor, Muhammad
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 1 (2026): April 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i1.2026.52-62

Abstract

The Central Bureau of Statistics (BPS) of Banjarmasin City faces challenges in managing incoming and outgoing correspondence, as the manual disposition process is time-consuming, paper-based, and prone to delays or document loss. To address these inefficiencies, a Web-Based Mail Disposition Application was developed to digitalize the document management process. The system allows users to record, track, and distribute correspondence efficiently through an integrated platform that supports automatic notifications and secure digital archiving. This study employs a descriptive qualitative approach, focusing on system design, implementation, and evaluation of its effectiveness in supporting administrative workflows. The results show that the web-based application significantly improves operational efficiency, reduces administrative errors, and enhances transparency and accountability in correspondence management. In conclusion, the adoption of digital document management systems contributes to optimizing work performance and supports the realization of effective, efficient, and environmentally friendly public services at BPS Banjarmasin.
Deteksi Kelelahan Pengendara Sepeda Motor Secara Multimodal Menggunakan Sinyal Denyut Nadi dan Postur Tubuh Berbasis Support Vector Machine: - Ichwana Putra, Dody; Adira , Alvira; Ekariani, Shelvi
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 1 (2026): April 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i1.2026.144-151

Abstract

Kelelahan pengendara sepeda motor merupakan salah satu faktor utama yang berkontribusi terhadap meningkatnya risiko kecelakaan lalu lintas. Penelitian ini mengusulkan sebuah sistem deteksi kelelahan pengendara sepeda motor berbasis multimodal dengan memanfaatkan sinyal denyut nadi dan postur tubuh sebagai indikator kondisi pengendara. Data denyut nadi dan sudut kemiringan tubuh diperoleh melalui perangkat wearable dan diproses pada sisi edge untuk mengekstraksi fitur-fitur penting. Selanjutnya, fitur multimodal tersebut diklasifikasikan menggunakan algoritma Support Vector Machine (SVM) untuk menentukan kondisi pengendara ke dalam tiga kelas, yaitu normal, mengantuk sedang, dan mengantuk berat. Hasil klasifikasi ditampilkan melalui aplikasi Android dan digunakan sebagai dasar pemberian peringatan kepada pengendara apabila terdeteksi kondisi kelelahan. Hasil pengujian menunjukkan bahwa sistem mampu mengklasifikasikan kondisi pengendara secara tepat dengan tingkat akurasi, presisi, recall, dan F1-score yang tinggi pada data pengujian. Sistem yang diusulkan berpotensi digunakan sebagai sistem peringatan dini untuk meningkatkan keselamatan pengendara sepeda motor, khususnya pada perjalanan dengan durasi yang panjang.
Deteksi Stres Teks Percakapan Digital Menggunakan Model LSTM Musadad, Agni; Sulastri, Heni
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 1 (2026): April 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i1.2026.152-159

Abstract

Early stress detection through digital conversational text is crucial for mental health, but research in Indonesian is still limited. This study designs and evaluates a Long Short-Term Memory (LSTM)-based deep learning model to classify Indonesian text as stressful or non-stressful. The model was trained and tested using a labeled dataset of 11,000 samples. The methodology included text preprocessing, model training, and sensitivity analysis of hyperparameters such as learning rate, batch size, and number of LSTM units to find the optimal configuration. The proposed model demonstrated strong performance with an accuracy of 86.48% and a balanced F1-Score of 0.87 (non-stress) and 0.86 (stress), outperforming several previous baselines. Training curve analysis identified clear overfitting, while hyperparameter sensitivity analysis revealed that the default configuration with 64 LSTM units was suboptimal—performance improved with the use of 128 LSTM units or a batch size of 128. This study confirms the effectiveness of LSTM for stress detection in Indonesian text, while also demonstrating the need for further hyperparameter optimization and the need for more robust overfitting handling techniques.
Klasifikasi Penyakit Jantung Berbasis Data Rekam Medis Menggunakan Algoritma Local Mean K-Nearest Neighbor Putri, Alayda Zaielamy; Daud, Muhammad; Aidilof, Hafizh Al Kautsar
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 1 (2026): April 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i1.2026.134-143

Abstract

Penyakit jantung merupakan penyebab kematian utama di dunia dengan diagnosis dini yang penting namun sering terkendala akurasi interpretasi data rekam medis kompleks. Algoritma klasifikasi tradisional seperti K-NN memiliki kelemahan dalam menangani noise dan outliers dalam data medis. Penelitian ini bertujuan mengimplementasikan algoritma Local Mean K-Nearest Neighbor untuk mengklasifikasikan penyakit jantung berdasarkan data rekam medis dengan akurasi yang lebih baik. Dataset terdiri dari 403 observasi dengan 10 variabel meliputi jenis kelamin, umur, tekanan darah, heart rate, respiratory rate, hasil elektrokardiogram, kondisi nyeri dada, dan klasifikasi diagnosis. Metode Local Mean K-NN mengadaptasi konsep K-NN tradisional dengan pendekatan local mean calculation untuk mengatasi noise dan outliers. Tahapan penelitian mencakup preprocessing data, feature encoding, feature scaling, hyperparameter tuning, dan evaluasi menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil menunjukkan algoritma Local Mean K-NN dengan nilai K optimal 11 mampu mengklasifikasikan penyakit jantung dengan accuracy 71.60%, precision 69.21%, recall 71.60%, dan F1-score 70.27%. Model menunjukkan performa sangat baik dalam mendeteksi Penyakit Jantung Koroner dengan precision 91.89% dan recall 97.14%. Analisis feature importance mengidentifikasi nyeri dada sebagai indikator terpenting (73.79%), diikuti heart rate (36.40%) dan respiratory rate (25.25%). Penelitian membuktikan efektivitas Local Mean K-NN sebagai clinical decision support tool dalam klasifikasi penyakit kardiovaskular meskipun terdapat tantangan class imbalance pada kelas minoritas.
Implementasi Random Forest Regression Untuk Prediksi Harga Saham Consumer Non-Cyclicals Berbasis Rasio Fundamental Debora, Devi; R.A.E. Virgana Targa Sapanji
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 1 (2026): April 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i1.2026.126-133

Abstract

Prediksi harga saham merupakan aspek krusial dalam pengambilan keputusan investasi, khususnya pada sektor Consumer Non-Cyclicals yang memiliki karakteristik permintaan relatif stabil. Namun, hubungan antara rasio fundamental dan harga saham sering bersifat non-linear sehingga sulit dimodelkan menggunakan pendekatan statistik konvensional. Penelitian ini bertujuan untuk membangun model prediksi harga saham berbasis algoritma Random Forest Regression dengan mengintegrasikan rasio fundamental dan fitur turunan hasil feature engineering pada sektor Consumer Non-Cyclicals di Bursa Efek Indonesia. Penelitian menggunakan data sekunder berupa laporan keuangan dan harga saham kuartalan perusahaan sektor Consumer Non-Cyclicals periode 2022–2024. Variabel independen meliputi EPS, Book Value, DER, ROA, ROE, dan NPM, serta fitur turunan seperti PER, PBV, interaksi rasio, harga lag, dan perubahan harga. Pemodelan dilakukan menggunakan Random Forest Regression dengan pembagian data TimeSeriesSplit. Evaluasi model menggunakan MAE, RMSE, dan R², serta interpretasi model dilakukan melalui Feature Importance dan SHAP. Hasil penelitian menunjukkan bahwa model Random Forest Regression memiliki kinerja prediksi yang baik dan mampu menangkap pola non-linear antara variabel fundamental dan harga saham. Fitur Harga_Lag1, ROE, PER, dan interaksi DER_ROA menjadi variabel paling berpengaruh dalam menentukan harga saham. Model yang dikembangkan efektif sebagai alat bantu prediksi harga saham berbasis fundamental dan berpotensi mendukung pengambilan keputusan investasi yang lebih akurat dan berbasis data.
Systematic Literature Review: Optimasi Keputusan Pada Monitoring Kesehatan Hutan Menggunakan Metode Multi-Objective Optimization By Ratio Analysis (MOORA) Andrianti, Ari; Bima Alfajri, Willy; yudistira, Miranty
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 1 (2026): April 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i1.2026.113-125

Abstract

Forest health monitoring is a crucial component of sustainable forest management, particularly amid increasing pressures from deforestation, land degradation, and climate change. The complexity of forest ecosystems means that decision-making processes cannot rely on a single indicator but instead require approaches capable of simultaneously accommodating multiple biophysical, environmental, and social criteria. Therefore, Multi-Criteria Decision Making (MCDM) methods have been widely applied in decision support systems within the forestry sector. This study aims to systematically examine the application of the Multi-Objective Optimization by Ratio Analysis (MOORA) method in optimizing decision-making for forest and environmental health monitoring. The research adopts a Systematic Literature Review (SLR) methodology based on the PRISMA guidelines. The SLR process includes the formulation of research questions, literature search strategies, the establishment of inclusion and exclusion criteria, quality assessment of selected studies, and narrative synthesis of the findings. Literature searches were conducted using Google Scholar, Scopus, and OpenAlex databases, covering articles published between 2021 and 2026. The selection results indicate that the MOORA method has been extensively applied in environmental and natural resource decision support systems, particularly for alternative ranking and the determination of forest and land management priorities. Overall, MOORA is considered effective in producing objective, consistent, and easily interpretable decisions, either as a standalone method or in combination with other approaches such as AHP, ORESTE, and GIS. However, most existing studies remain case-based with limited geographical scope and have not yet developed standardized forest health indicators. These findings highlight opportunities for further research to develop more comprehensive MOORA-based forest health monitoring models to support environmental management and sustainable forest management policies.

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