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All Journal Journal of Applied Engineering and Technological Science (JAETS) Journal of Applied Data Sciences Journal of Computer and Information System (J-CIS) Sisfo: Jurnal Ilmiah Sistem Informasi
Irwanda Syahputra
Universitas Samudra
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

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Sara Detection on Social Media Using Deep Learning Algorithm Development M. Khairul Anam; Lucky Lhaura Van FC; Hamdani Hamdani; Rahmaddeni Rahmaddeni; Junadhi Junadhi; Muhammad Bambang Firdaus; Irwanda Syahputra; Yuda Irawan
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.5390

Abstract

Social media has become a key platform for disseminating information and opinions, particularly in Indonesia, where SARA (Ethnicity, Religion, Race, and Intergroup) issues can fuel social tensions. To address this, developing an automated system to detect and classify harmful content is essential. This study develops a deep learning model using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to detect SARA-related comments on Twitter. The method involves data collection through web scraping, followed by cleaning, manual labeling, and text preprocessing. To address data imbalance, SMOTE (Synthetic Minority Over-sampling Technique) is applied, while early stopping prevents overfitting. Model performance is evaluated using precision, recall, and F1-score. The results demonstrate that SMOTE significantly improves model performance, particularly in detecting minority-class SARA comments. CNN+SMOTE achieves a accuracy of 93%, and BiLSTM+SMOTE records a recall of 88%, effectively capturing patterns in SARA and non-SARA data. With SMOTE and early stopping, the model successfully manages class imbalance and reduces overfitting. This research supports efforts to curtail hate speech on social media, especially in the Indonesian context, where SARA-related issues often dominate public discourse.
Sistem Informasi Pengarsipan Surat Seksi Tindak Pidana Khusus Di Kejaksaan Negeri Langsa Berbasis Website Badratunnafis Badratunnafis; Irwanda Syahputra
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 9 No. 2 (2025): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2025
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/.v9i2.26011

Abstract

Kejaksaan Negeri Langsa sebagai salah satu institusi penegak hukum memiliki tanggung jawab dalam melaksanakan tugas penuntutan, pelaksanaan putusan pengadilan, serta kewenangan lain sesuai ketentuan perundang-undangan. Salah satu bidang yang berperan penting adalah Bidang Seksi Tindak Pidana Khusus (Tipidsus) yang menangani kasus-kasus khusus seperti tindak pidana korupsi, pelanggaran undang-undang tertentu, dan kejahatan yang memerlukan penanganan intensif. Dalam menjalankan tugasnya, Bidang Tipidsus menerima, mengelola, dan menyimpan berbagai surat serta dokumen perkara dalam jumlah besar. Berdasarkan hasil observasi, proses pengarsipan di bidang tersebut sebagian besar masih dilakukan secara manual dalam bentuk dokumen fisik. Metode ini menimbulkan beberapa kendala, seperti risiko kerusakan atau kehilangan dokumen, kesulitan dalam pencarian data arsip, serta kebutuhan ruang penyimpanan yang besar sehingga dapat menghambat efektivitas kerja dan pelayanan informasi. Untuk mengatasi permasalahan tersebut, dikembangkan Sistem Informasi Pengarsipan Surat Seksi Tindak Pidana Khusus di Kejaksaan Negeri Langsa berbasis Website. Sistem ini dirancang menggunakan bahasa pemrograman PHP dan database MySQL dengan tujuan mempermudah proses pencatatan, penyimpanan, pencarian, dan pengelompokan arsip secara digital, cepat, dan akurat. Selain itu, sistem berbasis website memungkinkan akses terpusat, peningkatan keamanan melalui autentikasi pengguna, serta mendukung pengelolaan arsip yang lebih efisien dan modern. Hasil implementasi menunjukkan bahwa sistem mampu memperbaiki proses pengelolaan arsip surat, meminimalisasi risiko kehilangan data, dan meningkatkan efektivitas kerja pada Bidang Tipidsus Kejaksaan Negeri Langsa.
Application of Fuzzy Inference System Sugeno Model for Forecasting Antam Gold Prices in Indonesia: A Case Study of Monthly Data 2023–2025 Irwanda Syahputra; Alfa Saleh; Khairul Anam
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27233

Abstract

Antam gold prices represent one of the most volatile investment indicators in Indonesia, influenced by macroeconomic factors including rupiah exchange rates, global inflation, and geopolitical uncertainty. The ability to accurately forecast gold prices has become a strategic necessity for investors and market participants. This study applies a Fuzzy Inference System (FIS) with the Sugeno model to forecast Antam gold prices using monthly data from January 2023 to December 2025, comprising 36 data points. The input variables are gold prices from the previous month (t-1) and two months prior (t-2), while the output variable is the predicted price for period t. Data is split 75:25 for training and testing. Evaluation using Mean Absolute Percentage Error (MAPE) yields 2.14%, categorized as excellent accuracy. This study provides empirical evidence that the simple yet interpretable Fuzzy Sugeno method achieves high accuracy in commodity price time series forecasting.
Analisis Penjualan Terhadap Durasi Live Dan Jumlah Penonton Aktif Melalui Metode Optimasi Dengan Algoritma Stochastic Gradient Descent (Sgd) Ginda M. A. Siregar; Muhammad Farid Fitriansyah; Rizalul Akram; Irwanda Syahputra; Ade Zulkarnain Hasibuan
Journal of Computer and Information System ( J-CIS ) Vol. 9 No. 1 (2026): J-CIS Vol. 9 No. 1 Tahun 2026
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jcis.v9i1.6329

Abstract

Live streaming commerce menjadi strategi pemasaran digital yang memungkinkan interaksi langsung antara penjual dan konsumen secara real-time. Namun, peningkatan durasi siaran dan jumlah penonton aktif tidak selalu diikuti peningkatan jumlah produk terjual. Penelitian ini bertujuan membangun model prediksi jumlah produk terjual berdasarkan durasi live (X₁) dan jumlah penonton aktif (X₂) menggunakan regresi linear yang dioptimasi dengan algoritma Stochastic Gradient Descent (SGD). Dataset historis aktivitas live streaming digunakan sebagai data pelatihan dan evaluasi model. Kinerja model diukur menggunakan metrik R², Mean Absolute Error (MAE), Root Mean Square Error (RMSE), dan Mean Absolute Percentage Error (MAPE). Hasil pengujian menunjukkan model memperoleh nilai R² sebesar 0,4995, MAE sebesar 10,0113, RMSE sebesar 14,0243, dan MAPE sebesar 66,94%. Model kemudian diimplementasikan dalam dashboard berbasis Streamlit untuk menyediakan simulasi prediksi penjualan secara real-time guna mendukung perencanaan strategi live streaming yang lebih efektif dan berbasis data.
Improved Performance of Hybrid GRU-BiLSTM for Detection Emotion on Twitter Dataset M. Khairul Anam; Munawir Munawir; Lusiana Efrizoni; Nurul Fadillah; Wirta Agustin; Irwanda Syahputra; Tri Putri Lestari; Muhammad Bambang Firdaus; Lathifah Lathifah; Atalya Kurnia Sari
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.459

Abstract

This study addresses emotion detection challenges in tweets, focusing on contextual understanding and class imbalance. A novel hybrid deep learning architecture combining GRU-BiLSTM with SMOTE is proposed to enhance classification performance on an Israel-Palestine conflict dataset. The dataset contains 40,000 tweets labeled with six emotions: anger, disgust, fear, joy, sadness, and surprise. SMOTE effectively balances the dataset, improving model fairness in detecting minority classes. Experimental results show that the GRU-BiLSTM hybrid with an 80:20 data split achieves the highest accuracy of 89%, surpassing BiLSTM alone, which obtained 88%, and other state-of-the-art models. Notably, the proposed model delivers significant improvement in detecting the emotion of joy (recall: 0.87, F1-score: 0.86). In contrast, the surprise category remains challenging (recall: 0.24). Compared to existing research, this study highlights the effectiveness of combining SMOTE and hybrid GRU-BiLSTM, outperforming models such as CNN, GRU, and LSTM on similar datasets. The incorporation of GloVe embeddings enhances contextual word representations, enabling nuanced emotion detection even in sarcastic or ambiguous texts. The novelty lies in addressing class imbalance systematically with SMOTE and leveraging GRU-BiLSTM's complementary strengths, yielding superior performance metrics. This approach contributes to advancing emotion detection tasks, especially in conflict-related social media data, by offering a robust, context-sensitive, and balanced classification method.
Co-Authors Akram, Rizalul Alfa Saleh Anam, M Khairul Atalya Kurnia Sari Badratunnafis Badratunnafis Ginda M. A. Siregar Hamdani Hamdani Hasibuan, Ade Zulkarnain Junadhi Junadhi Lathifah Lathifah Lucky Lhaura Van FC, Lucky Lhaura Lusiana Efrizoni Muhammad Bambang Firdaus Muhammad Bambang Firdaus Muhammad Farid Fitriansyah Munawir Munawir Nurul Fadillah Rahmaddeni Rahmaddeni Tri Putri Lestari Wirta Agustin Yuda Irawan