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Adli Abdillah Nababan
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admitechsolutions@gmail.com
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+62 811 6556 192
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sisfotekjar@journal.itisd.org
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Jl. Pintu Air Gg. Langgar, Siti Rejo I, Kec. Medan Kota, Kota Medan, Sumatera Utara 20219
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INDONESIA
Jurnal Sistem Informasi dan Teknologi Jaringan
Published by CV. ADMITECH SOLUTIONS
ISSN : 28087917     EISSN : 28079259     DOI : https://doi.org/10.63703/sisfotekjar
Core Subject : Science,
Computer Science & IT
Jurnal SISFOTEKJAR adalah jurnal yang diterbitkan oleh CV. ADMITECH SOLUTIONS yang bertujuan untuk mewadahi hasil penelitian tentang Sistem Informasi dan Teknologi Jaringan . Jurnal SISFOTEKJAR (Sistem Informasi dan Teknologi Jaringan) adalah wadah informasi berupa hasil penelitian, studi kepustakaan, gagasan, aplikasi teori dan kajian analisis kritis dibidang berbagai ilmu. Jurnal SISFOTEKJAR (Sistem Informasi dan Teknologi Jaringan) terbit 2 kali dalam satu tahun yaitu di bulan Maret dan September.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 27 Documents
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Prediction of Digital Marketing Campaign Success Using Deep Neural Network Models Mayang Modelina Cynthia; Rahma Syahri; Muhammad Hafizh Al-Ghifari Rangkuti; Muhammad Akbar Firdaus; Rido Favorit Saronitehe Waruwu; Yasoziduhu Halawa; Tita Ritonga
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 6 No 2 (2025): September
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/sisfotekjar.v6i2.33

Abstract

The rapid growth of digital advertising platforms has generated large volumes of complex and nonlinear campaign performance data, making accurate prediction of campaign success increasingly challenging. Traditional machine learning approaches often struggle to fully capture these nonlinear relationships. Therefore, this study proposes a Deep Learning approach using a Deep Neural Network (DNN) to predict the success of digital marketing campaigns based on key performance indicators such as impressions, clicks, CTR, CPC, CPM, engagement rate, and conversions.This research follows the CRISP-DM framework, including data understanding, preprocessing, model development, training, and evaluation. The dataset was obtained from digital advertising platform performance reports and processed through data cleaning, feature scaling, and train–test splitting. The proposed DNN model consists of multiple fully connected layers with ReLU activation functions and is optimized using the Adam optimizer. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC.The experimental results show that the proposed Deep Learning model achieves an accuracy of 87.6%, precision of 86.9%, recall of 85.8%, F1-score of 86.3%, and ROC-AUC of 0.91, indicating strong predictive performance. These findings demonstrate that Deep Learning effectively captures complex patterns in digital marketing data and provides reliable insights to support data-driven marketing decision-making.
Implementing a Risk-Based Hiring System Using the Spiral Model at PT. Indonesia Gadai Oke Muhammad Adam Gozali; Martiano
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 6 No 2 (2025): September
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/sisfotekjar.v6i2.107

Abstract

This study aims to address the inefficiencies and risks in the manual recruitment process at PT. Indonesia Gadai Oke, which often leads to data inconsistency, document loss, and subjective evaluations. To overcome these challenges, a Hiring Management System was developed using the Software Development Life Cycle (SDLC) with a Spiral Model approach that emphasizes risk analysis and mitigation throughout the development process. The system’s effectiveness was evaluated by applying Failure Mode and Effect Analysis (FMEA) both before and after its implementation to identify, assess, and prioritize risks based on the Risk Priority Number (RPN). The results show that the implementation of the risk-based Hiring Management System significantly reduced the RPN across all recruitment stages — the highest initial RPN in the manual process, which was 336 (for application reception), decreased to 60 after system implementation. This indicates that the system effectively minimizes potential failures, increases efficiency, and enhances objectivity in the recruitment process. In conclusion, the risk-based Hiring Management System developed using the Spiral Model and evaluated through FMEA has proven effective in mitigating critical risks and improving the overall recruitment process at PT. Indonesia Gadai Oke.
Penggunaan Deep Learning untuk Mengklasifikasi  Hate speech dan Good Speech Terhadap Pertamina di Platform Twitter dengan Metode Convolutional Neural Network (CNN) Hasan A. Situmorang; Martiano
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 6 No 2 (2025): September
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/sisfotekjar.v6i2.108

Abstract

Advances in digital technology have changed the way people express their opinions, particularly through social media platforms like Twitter. In social and corporate contexts, Pertamina, a state-owned energy company in Indonesia, is frequently the subject of public discourse, both in the form of positive (good speech) and negative (hate speech) expressions. To manage this information, a system capable of accurately and automatically classifying tweets is crucial. This study aims to develop a Deep Learning-based text classification model, specifically using the Convolutional Neural Network (CNN) method, to identify tweets containing hate speech and good speech related to Pertamina. Data was collected from Twitter using relevant keywords, followed by manual preprocessing and labeling. The cleaned dataset was then divided into training and testing data for processing using a CNN architecture. The results showed that the CNN model performed very well in the classification task, achieving a validation accuracy of 98.08% and a testing accuracy of 97.66%. Evaluation using a confusion matrix also showed high precision and recall values, with an f1 score of 0.98. These findings demonstrate that the CNN method is effective in accurately identifying hate speech and positive speech in Indonesian text data, particularly regarding issues related to Pertamina. This research is expected to contribute to the development of automated social media monitoring systems and public opinion management tools.
Penerapan Metode Algoritma Extreme Gradient Boosting Dalam Memprediksi Penjualan Thrifting Pada Toko Cchase Muhammad Ichwan Gifari; Yoshida Sary
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 6 No 2 (2025): September
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/sisfotekjar.v6i2.109

Abstract

Thrifting nowadays is very easy to access since thrift stores have been rapidly emerging in urban areas. However, one of the common problems faced by thrift shops is related to stock management and sales prediction. For example, at Cchase store, stock estimation is often inaccurate due to the wide variety of thrift items and the uncertain supply, which leads to the risk of unsold inventory. To overcome this issue, this study applies machine learning technology using the Extreme Gradient Boosting (XGBoost) algorithm. This method was chosen because it has been proven to provide accurate prediction results on data with complex patterns. The data used consist of one year of sales records, which were processed, split into training and testing sets, and then evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. The results show that the model achieved an RMSE of 0.8105 and an MAE of 0.6643, indicating that the model performs well in predicting sales. Furthermore, the prediction results for the upcoming month reveal the top three product categories with the highest sales, namely crewnecks, hoodies, and t-shirts. These findings are expected to help thrift business owners manage stock more efficiently and develop more effective sales strategies.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI MANAJEMEN INVENTARIS MENGGUNAKAN METODE RAPID APPLICATION DEVELOPMENT (RAD) PADA PT. INDONESIA GADAI OKE Fadli Caniago; Martiano
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 6 No 2 (2025): September
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/sisfotekjar.v6i2.111

Abstract

Ketidakefisienan dalam pengelolaan inventaris barang di PT. Indonesia Gadai Oke, terutama akibat pencatatan manual, telah menimbulkan permasalahan seperti kehilangan data, keterlambatan laporan, dan kesulitan dalam monitoring barang. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem informasi manajemen inventaris berbasis web dengan pendekatan Rapid Application Development (RAD). Data kebutuhan sistem diperoleh melalui wawancara, observasi, dokumentasi, dan divalidasi dengan referensi teori yang relevan. Proses pengembangan mengikuti tahapan RAD yang terdiri dari perencanaan kebutuhan, desain prototipe, pengembangan sistem, dan implementasi. Sistem dibangun menggunakan framework Yii2 dan database MySQL, serta diuji melalui metode blackbox dan pengujian keamanan. Hasil implementasi menunjukkan bahwa sistem mampu menangani pencatatan data barang, transaksi peminjaman dan pengembalian, pelaporan otomatis dalam format PDF, monitoring kondisi barang melalui grafik, serta pengaturan hak akses pengguna berbasis peran. Sistem ini dinilai layak untuk digunakan dan dapat meningkatkan efisiensi serta akurasi dalam manajemen inventaris perusahaan.
Perancangan Visual Dan Evaluasi Usability Pada Sistem Visualisasi Interaktif Untuk Akses Informasi Publik Irwansyah; Leonardo Tanuwijaya; Kristian Heri Natal Lumban Batu; Mirza Ilhami; Culita; Riche; Khairani Puspita
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 7 No 1 (2026): Maret
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/sisfotekjar.v7i1.198

Abstract

Penyajian informasi publik melalui kanal digital sering menghadapi persoalan kepadatan data, hierarki informasi yang lemah, dan variasi literasi digital pengguna. Penelitian ini bertujuan merancang prototipe dashboard visual interaktif dan melakukan evaluasi usabilitas awal untuk mengidentifikasi kualitas navigasi, kejelasan visual, pemahaman informasi, dan kepuasan pengguna. Perancangan menggunakan pendekatan user-centered design melalui identifikasi konteks, analisis kebutuhan, pengembangan konsep komunikasi visual, implementasi prototipe, dan evaluasi formatif. Pengujian melibatkan 30 calon pengguna dewasa yang direkrut secara purposif. Peserta menjalankan skenario pencarian indikator layanan, penggunaan filter data, serta interpretasi grafik, peta, dan tabel. Evaluasi menggunakan empat dimensi penilaian deskriptif pada skala 0–100 dan umpan balik kualitatif; instrumen tersebut tidak diposisikan sebagai System Usability Scale (SUS). Hasil menunjukkan skor kemudahan navigasi 82, kejelasan visual 85, pemahaman informasi 88, dan kepuasan pengguna 84, dengan rerata deskriptif 84,75 atau 84,8 setelah pembulatan. Navigasi menjadi dimensi terendah dan mengarahkan rekomendasi pada penguatan status filter, petunjuk interaksi, serta konsistensi kontrol. Temuan memberikan bukti awal bahwa integrasi prinsip desain komunikasi visual dan evaluasi berbasis pengguna dapat mendukung penyajian informasi publik yang lebih terstruktur. Kesimpulan dibatasi pada prototipe dan sampel penelitian ini tanpa klaim kausal maupun perbandingan dengan penyajian teks.
Analisis Sentimen Ulasan Wisata Sumenep dengan SVM dan TF-IDF M. Yahya Ubaidillah; Saiful Bahri
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 7 No 1 (2026): Maret
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/sisfotekjar.v7i1.199

Abstract

Penelitian ini bertujuan mengklasifikasikan sentimen ulasan destinasi wisata di Kabupaten Sumenep menggunakan pembobotan Term Frequency–Inverse Document Frequency (TF-IDF) dan algoritma Support Vector Machine (SVM), serta menilai kinerja model pada distribusi kelas yang tidak seimbang. Data diperoleh melalui web scraping ulasan enam destinasi wisata pada Google Maps. Dari 797 ulasan yang terkumpul, sebanyak 725 ulasan digunakan setelah data tanpa teks dan ulasan dengan peringkat tiga dikeluarkan. Peringkat empat dan lima diberi label positif, sedangkan peringkat satu dan dua diberi label negatif. Praproses teks meliputi case folding, pembersihan teks, normalisasi kata, penghapusan stopword, dan stemming. Teks kemudian direpresentasikan menggunakan TF-IDF dengan maksimum 3.000 fitur berupa unigram dan bigram. Klasifikasi dilakukan menggunakan SVM dengan kernel linear, parameter , dan bobot kelas balanced. Dataset akhir terdiri atas 643 ulasan positif dan 82 ulasan negatif. Model menghasilkan akurasi 87,50%, nilai F1 tertimbang 85,61%, nilai F1 makro 59,09%, dan balanced accuracy 57,42%. Recall kelas positif mencapai 96,09%, sedangkan recall kelas negatif hanya 18,75%. Model menunjukkan performa yang tinggi dalam mengenali ulasan positif, tetapi belum memadai dalam mendeteksi ulasan negatif. Ketimpangan kelas dan keterbatasan variasi ulasan negatif masih memengaruhi hasil klasifikasi, sehingga evaluasi model perlu mempertimbangkan metrik per kelas, bukan hanya akurasi keseluruhan.

Page 3 of 3 | Total Record : 27

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