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All Journal JUSIFO : Jurnal Sistem Informasi Jurnal Ilmiah KOMPUTASI JUTIM (Jurnal Teknik Informatika Musirawas) JOISIE (Journal Of Information Systems And Informatics Engineering) Journal of Information Systems and Informatics Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Sistem Komputer dan Informatika (JSON) Journal of Information Technology Ampera Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Ferdiansyah Ferdiansyah
Universitas Bina Darma
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy Engineering Other

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Implementasi Algoritma Frequent Growth (FP-Growth) Menentukan Asosiasi Antar Produk Rangga Yogasuwara; Ferdiansyah Ferdiansyah
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 1 (2022): September 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4894

Abstract

Data accumulation is caused by the amount of transaction data stored. By utilizing the sales transaction data in the database, the data can be further processed into useful information for managers to make decisions. With the existence of data mining, it is hoped that it can help the Leaning Shop to find the information contained in the transaction data into new knowledge. Association Rule, which is a procedure in Market Basket Analysis to find relationships between items in a data set or it can be said that this association rule aims to find a collection of items that often appear at the same time and display them in the form of consumer habits in shopping. The FP-Growth algorithm is an algorithm that can be used to determine the data set that appears most often (frequent itemset) in a data, in the search for frequent itemset in a data set by generating a prefix-tree structure or often called the FP-Tree. From the test results it can be concluded that the application of data mining using the FP-Growth Algorithm can be used to analyze consumer spending patterns.
IMPLEMENTASI ALGORITMA NAÏVE BAYES PADA SISTEM PAKAR DIAGNOSA PENYAKIT TIFOID BERBASIS WEB Ilham budiman; Ferdiansyah
JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer Vol 13 No 3 (2022): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : LPPM Sekolah Tinggi Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v13i3.549

Abstract

Typhoid is an infectious disease that is still a health problem in developing countries, especially in Indonesia. Salmonella typhi is a bacterium that causes typhoid fever which can be transmitted through food or drink contaminated with feces or urine from an infected person. The first step in the management of typhoid is the establishment of an appropriate diagnosis. The expert system application for diagnosing diarrheal disease is made with a web-based application, so that it can be accessed by the wider community, besides this application can also help medical personnel to make decisions in the diagnosis of typhoid. The advantage of the nave Bayes algorithm in this expert system is that it can increase calculations ranging from nausea and vomiting, diarrhea, sore throat, headache, fever and loss of appetite, so obtained 14 rules resulting from interviews with experts, so it can be concluded that the research conducted implemented into a web application can assist users in diagnosing typhoid disease in each patient.
Prediksi Mata Uang Bitcoin Menggunakan LSTM Dan Sentiment Analisis Pada Sosial Media Andreean Dharma Arisandi; Ferdiansyah; Linda Atika; Edi Surya Negara; Kiki Rizky Nova Wardani
Jurnal Ilmiah Komputasi Vol. 19 No. 4 (2020): Jurnal Ilmiah Komputasi Volume: 19 No. 4, Desember 2020
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.19.4.370

Abstract

Cryptocurrency adalah mata uang digital dimana transaksi dapat dilakukan dengan transaksi online. Salah satu jenisnya yaitu bitcoin. Bitcoin adalah salah satu mata uang elektronik yang bersifat desentralisasi (tidak terpusat) dan tidak diatur atau dijamin oleh otoritas pusat. Harga Bitcoin sangat ekuktuatif dan sering kali membuat resah pengguna dan investor Bitcoin. Oleh karena itu, diusulkan sebuah metode atau sistem prediksi harga Bitcoin dengan mempelajari pola dan tingkah laku data time series harga historisnya. Dalam Penelitian ini, kontribusi utamanya yaitu analisis sentimen yang dapat membedakan tweet positif dan negatif dari bitcoin di twitter dengan akurasi 80.00%. Dengan model LSTM yang dapat memprediksi harga Bitcoin pada hari berikutnya dengan mempertimbangkan harga historis dan skor sentimen positif dan negatif. Namun teknik ini memerlukan parameter yang tepat untuk mendapatkan hasil prediksi yang akurat. Hasil yang didapatkan dari penelitian ini, menunjukkan jika sistem yang akan dibangun nantinya dapat melihat nilai bitcoin dengan lebih baik lagi. Setelah di evaluasi dengan RMSE didapatkan nilai 335.201882 dengan epoch 10. Semakin kecil RMSE maka semakin baik performansi modelnya terhadap data testing
Analisis Tingkat Akurasi Prediksi Gejala COVID - 19 Dengan Menggunakan Metode Logistic Regression dan Support Vector Machine Briandy Tri Putra Briandy; Evi Yulianingsih; Fatmasari; Ferdiansyah
JURNAL FASILKOM Vol 13 No 02 (2023): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v13i02.5629

Abstract

Salah satu teknologi ilmu komputer yang deprogram untuk mempelajari dan melakukan aktivitas seperti manusia adalah kecerdasan buatan. Teknologi kecerdasan buatan telah dipakai pada beberapa bidang, salah satunya di bidang kesehatan. Dibidang kesehatan, kecerdasan buatan digunakan sebagai alat untuk mendeteksi penyakit pada manusia, salah satu contohnya adalah memprediksi gejala awal COVID-19 merupakan salah satu penyakit menular SARS-CoV2 yang menyebabkan pandemi di seluruh dunia, dan virus tersebut terdeteksi pertama kali dari hewan-hewan liar di pasar Kota Wuhan, China pada akhir 2019. Pada penelitian sebelumnya yang berjudul Metode Klasifikasi Gejala Penyakit Coronavirus Disease 19 (COVID-19) Menggunakan Algoritma Neural Network” oleh Rahmi, dkk, menggunakan data gejala-gejala COVID-19 untuk mendapatkan tingkat akurasi dalam prediksi COVID-19 menggunakan metode Neural Network dan Logistic Regression. Hasil penelitian tersebut mendapatkan tingkat akurasi sebesar 95% dengan metode Neural Network, dan 94% dengan metode Logistic Regression. Pada penelitian ini, penulis ingin membandingkan metode Logistic Regression dengan Support Vector Machine dalam memprediksi gejala awal COVID-19. Hasil dari penelitian ini adalah mendapatkan akurasi dengan tingkat yang tertinggi dari kedua metode tersebut.
PERANCANGAN DAN IMPLEMENTASI SISTEM MONITORING PEGAWAI PADA DINAS PERHUBUNGAN PROVINSI SUMATERA SELATAN BERBASIS MOBILE Rezky, Muhammad; Syah, Ferdian
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol 7 No 2 (2023)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v7i2.3500

Abstract

Pelaksanaan tugas lapangan oleh pegawai Dinas Perhubungan Provinsi Sumatera Selatan yang tersebar di lokasi yang berbeda menjadi kendala dalam proses monitoring yang dilakukan oleh pimpinan. Hal ini menyebabkan monitoring kinerja pegawai menjadi kurang efektif dan berpotensi mengakibatkan kurangnya kedisiplinan pegawai dalam menjalankan tugas yang berada di lapangan, karena tidak ada pengawasan secara langsung dari kepala bidang. Keadaan ini juga memungkinkan terjadinya pelanggaran yang tidak terdeteksi secara tepat waktu. Melihat permasalahan tersebut, maka solusinya adalah sebuah sistem monitoring pegawai yang dapat mencatat keberadaan pegawai ketika bertugas di lapangan. Pada penelitian ini metode yang digunakan adalah metode waterfall, yang mana merupakan metode pengembangan yang pelaksanaannya dilakukan dengan cara yang bertahap sehingga meminimalisir kesalahan yang mungkin akan terjadi. Hasil akhir dari penelitian ini adalah menghasilkan sistem informasi monitoring pegawai Dinas Perhubungan Provinsi Sumatera Selatan Bagian Lalu Lintas berbasis mobile yang dapat mempermudah dalam mencatat keberadaan pegawai ketika bertugas di lapangan.
Machine Learning Models for DDoS Detection in Software-Defined Networking: A Comparative Analysis Ferdiansyah, Ferdiansyah; Antoni, Darius; Valdo, Muhammad; Mikko, Mikko; Mukmin, Chairul; Ependi, Usman
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.864

Abstract

In today's digital age, Software-Defined Networking (SDN) has become a pivotal technology that improves network control and flexibility. Despite its advantages, the centralized nature of SDN also makes it susceptible to threats such as Distributed Denial of Service (DDoS) attacks. This study compares the effectiveness of three machine learning models Random Forest, Naive Bayes, and Linear Support Vector Classification (LinearSVC) using the 'DDoS SDN dataset' from Kaggle, which contains 104,345 records and 23 features. An equal 70/30 ratio was used on model. The models were then assessed using measures such as accuracy, precision, recall, and F1-score, and ROC curves. Among the models, Random Forest outperformed the others with a 97% accuracy, precision values of 1.00 (benign traffic) and 0.94 (malicious traffic), and an ROC AUC score of 1.00. In contrast, Naive Bayes and LinearSVC recorded lower accuracies of 63% and 66%, respectively. These findings underscore Random Forest's effectiveness in detecting DDoS attacks within SDN environments.
Analisis Prediksi Jangka Panjang COVID 19 Fase ke 3 di Indonesia menggunakan Deep Learning Herferry, Ibrahim Ade; Ferdiansyah, F; Kunang, Yesi Novaria; Purnamasari, Susan Dian
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.474

Abstract

This research is motivated by the ongoing impact of the COVID-19 pandemic, which continues to pose challenges for Indonesia, affecting both the economy and daily life. Therefore, this study will discuss long-term predictions for the third phase of COVID-19 in Indonesia using a Deep Learning model. The analysis aims to assist various stakeholders in developing better planning strategies to address COVID-19 in Indonesia. In conducting this research, the author employs neural networks to create a hybrid model combining GRU and LSTM algorithms. Utilizing RMSE and MAPE values, it can be concluded that the model's performance in predicting COVID-19 cases is influenced by the number of epochs used. Furthermore, the model demonstrates optimal performance at 150 epochs for predicting the number of COVID-19 cases in the next 7 days
Application of the LSTM Algorithm in Predicting Urea Fertilizer Production at IIB Plant PT. Pupuk Sriwidjaja Palembang Awaludin, Aziz; Ferdiansyah, F; Andri, A; Oktarina, Tri
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.704

Abstract

PT. Pupuk Sriwidjaja Palembang is a pioneer of fertilizer manufacturers in Indonesia. One of the plants at PT. Pupuk Sriwidjaja Palembang, namely the IIB urea plant, has been operating normally since 2017, thereby the data of production results has been collected for more than five years (time series data). The collected data can be used to make predictions of future production using the LSTM (Long Short Term Memory) model. LSTM is an artificial neural network architecture that is suitable for processing sequential data. The research objective to be achieved is to produce a production prediction model using LSTM modeling. Data collected over five years was divided into training data and testing data through data composition trials. The LSTM model training was carried out with a training data composition of 70% of the total data, batch size 64, and epoch 200. Then testing was carried out with data testing as much as 30% of the total data using RMSE and MAPE as model quality assessment parameters. Based on test results, the LSTM model is able to predict production with an RMSE of 11.08 and a MAPE of 6.39%.
Advanced Techniques for Anomaly Detection in Blockchain: Leveraging Clustering and Machine Learning Ferdiansyah, Ferdiansyah; Ependi, Usman; Tasmi, Tasmi; Haikal, Muhammad; Mikko, Mikko
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1047

Abstract

Blockchain technology has revolutionized data security and transaction transparency across various industries. However, the increasing complexity of blockchain networks has led to anomalies that require further investigation. This study aims to analyze anomalies in blockchain systems using machine learning approaches. Various anomaly detection techniques, including supervised and unsupervised methods, are evaluated for their effectiveness in identifying irregularities. The results indicate that machine learning models can detect anomalies with high accuracy, providing insights into potential threats and system vulnerabilities. The findings of this research contribute to improving blockchain security and developing more robust monitoring systems.
Forensic Analysis of AI-Generated Image Alterations Using Metadata Evaluation, ELA, and Noise Pattern Analysis Ferdiansyah, Ferdiansyah; Deazwara, Muhammad Rizki Akbar; Billanivo, Reynaldi Rizki; Ardiansyah, M.; Ilham, Ilham
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1362

Abstract

This study develops a forensic workflow to assess the authenticity of digital images, addressing the challenge of distinguishing AI-generated content from real photographs. The goal is to analyze metadata, compression behavior, and noise characteristics to identify synthetic images. The dataset includes eight images: two original Xiaomi 14T Pro photos and six AI-generated variants from Gemini, ChatGPT, and Copilot. Metadata was extracted using ExifTool version 13.25 on Kali Linux, while Error Level Analysis (ELA) and Noise Pattern Analysis (NPA) were performed with consistent parameters on the Forensically platform. Authentic images displayed complete EXIF metadata, uniform compression patterns, and stochastic sensor noise. In contrast, AI-generated images lacked EXIF data, included XMP or C2PA provenance, exhibited localized compression anomalies, and showed smoother, more structured noise patterns. The study presents a practical and reproducible forensic workflow that integrates metadata evaluation, ELA, and noise analysis to detect synthetic content. The findings demonstrate that despite their visual realism, AI-generated images still leave detectable forensic traces, offering valuable tools for image authenticity verification.
Co-Authors Aan Restu Mukti, Aan Restu Adam Prasetya Andreean Dharma Arisandi Andri, A Antoni, Darius Awaludin, Aziz Billanivo, Reynaldi Rizki Briandy Tri Putra Briandy Deazwara, Muhammad Rizki Akbar Deris Stiawan Edi Surya Negara Evi Yulianingsih Fani Prasetya Fatmasari Fatmasari Gege Ardiyansyah Herferry, Ibrahim Ade Ilham budiman Ilham Ilham Kiki Rizky Nova Wardani kiky rizky nova wardani Linda Atika Linda Atika M. Ardiansyah M. Daffa Alkhussayid Mikko, Mikko Muhammad Ashardiansyah Putra Muhammad Haikal Mukmin, Chairul Nurwijayanti Oktarina, Tri Raja Zahilah Md Radzi Rangga Yogasuwara Rezky, Muhammad Siti Hajar Othman Susan Dian Purnamasari, Susan Dian Sutra Ovi Yansa Usman Ependi Valdo, Muhammad Winoto Chandra Yeni Widyanti Yesi Novaria Kunang