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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jupiter Jurnal INKOM PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal technoscientia Jurnal Intelektualita: Keislaman, Sosial, dan Sains POSITIF Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) SMATIKA KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOIN (Jurnal Online Informatika) Jurnal Ilmiah KOMPUTASI Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Syntax Literate: Jurnal Ilmiah Indonesia CogITo Smart Journal Jurnal Ilmiah Matrik INOVTEK Polbeng - Seri Informatika Jusikom : Jurnal Sistem Komputer Musirawas JURNAL INSTEK (Informatika Sains dan Teknologi) IRJE (Indonesian Research Journal in Education) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Informatika Universitas Pamulang Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Informasi MURA Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Ilmiah Media Sisfo J-SAKTI (Jurnal Sains Komputer dan Informatika) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Informatika Global EDUMATIC: Jurnal Pendidikan Informatika JUSIM (Jurnal Sistem Informasi Musirawas) Jurnal Tekno Kompak Jurnal Mantik Jurnal Muara Ilmu Ekonomi dan Bisnis Journal of Information Systems and Informatics Zonasi: Jurnal Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknologi Informatika dan Komputer JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Djtechno: Jurnal Teknologi Informasi KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian kepada Masyarakat Bina Darma Jurnal Locus Penelitian dan Pengabdian Jurnal Bina Komputer Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Jurnal Ilmiah Ilmu Terapan Universitas Jambi International Journal of Advanced Science Computing and Engineering Innovative: Journal Of Social Science Research Bulletin of Social Informatics Theory and Application Jurnal Teknologi Informasi Mura Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Edi Surya Negara
Universitas Bina Darma
Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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User Satisfaction and Application Usage of PA'KEPO: A UTAUT 2 Model Analysis Agam, Padel Mohammad; Negara, Edi Surya; Andryani, Ria
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.888

Abstract

The PA'KEPO (Payo jadi Keluargo Polisi) application is a mobile Android application owned by Polda Sumsel for the recruitment process of police members. This study aims to evaluate user satisfaction and the use of the PA'KEPO application by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) 2 model with the addition of the Perceived Trust variable, which represents users' trust in the security and reliability of the application, enhancing users' intention to use the application and impacting actual usage behavior. The analysis of the relationships between variables employs the Structural Equation Modeling (SEM) approach to test the complex relationships between latent variables, allowing for the analysis of data with diverse scales. The research results indicate that the majority of respondents experienced a high level of satisfaction with the PA'KEPO application. The most influential variables—Effort Expectancy, Perceived Trust, Hedonic Motivation, Habit, and Behavioral Intention—significantly affect Use Behavior. Based on these findings, it is recommended that Polda Sumsel continue to encourage the use of the PA'KEPO application by optimizing the variables that have not yet shown significant effects. Improvement recommendations include evaluating and enhancing the application's functionality in line with the daily needs of its users.
Enhancement Support Vector Regression Using Black Widow Optimization for Predicting Foreign Exchange Rate Bhagaskara, -; Negara, Edi Surya
International Journal of Advanced Science Computing and Engineering Vol. 4 No. 3 (2022)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (820.687 KB) | DOI: 10.62527/ijasce.4.3.96

Abstract

Prediction of foreign exchange rates is one of the time series problems that have fluctuating value movements. There are several algorithms that can make predictive models for this problem, one of which is Support Vector Regression (SVR). In this study, foreign exchange rate predictions were made using Hybrid SVR and Black Widow Optimization (BWO). This is done with the aim of improving the performance of the SVR in order to produce a better predictive model for the EUR/USD foreign exchange rate data in 2020. The results of the proposed algorithm get better performance in terms of R2, MSE, RMSE, MAE, and MAPE compared to SVR.
Analisis Kepuasan Implementasi E-government Menggunakan Metode Webqual 4.0 dalam Pelayanan Publik di Bidang Kependudukan dan Catatan Sipil Kota Palembang Hendri; Surya Negara, Edi; Sutabri, Tata
Jurnal Informatika Universitas Pamulang Vol 9 No 3 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v9i3.43612

Abstract

Analisis kepuasan pengguna terhadap layanan sistem informasi berbasis elektronik atau e-government yang disediakan oleh lembaga pemerintah diperlukan untuk mengevaluasi kualitas layanan tersebut, terutama jika layanan ini merupakan kebutuhan vital dan memainkan peran penting dalam kemajuan masyarakat. Tujuan dari penelitan ini adalah untuk menggunakan pendekatan webqual 4.0, yang telah banyak digunakan untuk melakukan analisis atau pengukuran layanan publik. menggunakan regresi linier berganda untuk menentukan apakah dua atau lebih variabel independen mempengaruhi Y. Kualitas informasi, kualitas interaksi layanan, dan kualitas kegunaan adalah tiga variabel yang digunakan dalam uji data ini karena penulis hanya perlu menggunakan tiga variabel untuk menguji hipotesis dan mengevaluasi situs web dinas kependudukan dan pencatatan sipil kota Palembang dengan mengajukan beberapa pertanyaan kecil kepada responden. Hasil pengujian bersama menunjukkan bahwa variabel kualitas kegunaan dan interaksi layanan berpengaruh terhadap kepuasan pengguna, variabel kualitas informasi bernilai negatif, yang menunjukkan bahwa tidak ada pengaruh terhadap kepuasan pengguna, dan variabel kualitas kegunaan bernilai positif, yang menunjukkan bahwa ada pengaruh terhadap kepuasan pengguna.
MODEL DATA MINING DALAM MENGANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI KEMISKINAN DI SUMATERA SELATAN Hardianto, Hardianto; Novaria Kunang, Yesi; Surya Negara, Edi; Sutabri, Tata
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 2 (2025): JATI Vol. 9 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i2.12989

Abstract

Pengentasan kemiskinan menjadi isu strategis dalam Rencana Pembangunan Nasional Indonesia, dengan target pengurangan kemiskinan ekstrim menjadi nol persen pada tahun 2024. Penelitian ini berfokus pada analisis kemiskinan di Provinsi Sumatera Selatan, yang meskipun memiliki potensi ekonomi tinggi, masih menghadapi tantangan besar dengan tingkat kemiskinan yang mencapai 11,78 persen pada tahun 2023. Melalui pendekatan multidimensi dan metodologi data mining, penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang mempengaruhi kemiskinan serta memberikan alternatif visualisasi data untuk mempermudah interpretasi hasil analisis. Metode yang digunakan dalam penelitian ini adalah analisis data mining dengan algoritma machine learning, termasuk regresi linier, decision tree, dan random forest. Data yang dianalisis berasal dari Survei Sosial Ekonomi Nasional (Susenas) Maret 2023. Hasil penelitian menunjukkan bahwa variabel aksesibilitas, pengeluaran konsumsi makanan, dan luas lantai per kapita merupakan faktor dominan yang mempengaruhi tingkat kemiskinan di wilayah tersebut. Hal ini memberikan wawasan penting bagi pengambil kebijakan dalam merancang intervensi yang lebih efektif untuk pengentasan kemiskinan. Pemanfaatan visualisasi data dalam penelitian ini tidak hanya mengidentifikasi faktor-faktor yang signifikan, tetapi juga mendukung pemahaman yang lebih baik tentang situasi kemiskinan di Sumatera Selatan.
Analisa Dan Perbandingan Metode Klasterisasi Untuk Mengelompokkan Koleksi Buku Perpustakaan Adila, Nia; Novaria Kunang, Yesi; Herdiansyah, Izman; Negara, Edi Surya
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 5 (2025): JPTI - Mei 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.766

Abstract

Pada penelitian ini akan berfokus pada data buku perpustakaan Universitas Bina Darma. Data tersebut disimpan berupa catalog buku dan hanya diupdate pada setiap pelaporannya pertahun. Dari banyak data catalog buku yang ada pihak perpustakaan kurang memanfaatkan data tersebut sehingga peneliti tertarik menganalisa data buku pada perpustakaan Universitas Bina Darma, adanya proses Analisa pada data akan menggunakan perbandingan dua metode pada perpustakaan Universitas Bina Darma kemudian akan menghasilkan sebuah perbandingan pengelompokan informasi data buku dan kemudian akan dibandingkan menggunakan dua metode pengelompokan klasterisasi pada Perpustakaan Universitas Bina Darma yaitu metode K-Means dan K-Medoids. Penelitian akan menghasilkan perbandingan antara setiap variable dari dataset yang sudah ada yaitu pertama proses general untuk mengeksplorasi variabel tentang buku-buku yang ada dalam data ini secara umum dan menghasilkan 8 perbandingan cluster. Kemudian akan dilanjutkan pada beberapa proses untuk hasil implementasi menggunakan dua metode K-Means dan metode K-Medoids. Kemudian data di preprocessing dengan memanfaatkan software microsoft excel dan google colab. Maka penelitian ini dapat menjadi sebuah pengambil keputusan dan perbandingan cluster dari data Perpustakaan Universitas Bina Darma.
Sentiment and Emotion Classification Model Using Hybrid Textual and Numerical Features: A Case Study of Mental Health Counseling Ramayanti, Indri; Hermawan, Latius; Syakurah, Rizma Adlia; Stiawan, Deris; Meilinda, Meilinda; Negara, Edi Surya; Fahmi, Muhammad; Ghiffari, Ahmad; Rizqie, Muhammad Qurhanul
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.632

Abstract

Mental health issues among individuals, particularly in counseling contexts, require practical tools to understand and address emotional states. This study explores the application of machine learning models for emotion detection in mental health counseling conversations, focusing on four algorithms: Bernoulli Naive Bayes, Decision Tree, Logistic Regression, and Random Forest. The dataset, derived from transcribed counseling sessions, underwent preprocessing, including stemming, stopword removal, and TF-IDF vectorization to create structured inputs for classification. Emotional categories such as "Depresi" (Depression), "Kecewa" (Dissapointed), "Senang" (Happy), "Bingung" (Confused) and "Stres" (Stress) were analyzed to evaluate model performance. Results indicated that Logistic Regression achieved the highest accuracy at 82%, showcasing its reliability and scalability, followed closely by Random Forest with 81%, demonstrating robustness in handling complex data structures. Bernoulli Naive Bayes performed competitively at 80%, excelling in computational efficiency, while Decision Tree recorded the lowest accuracy at 70%, reflecting its limitations in managing overlapping features and high-dimensional data. These findings highlight the potential of machine learning in addressing the increasing demand for scalable mental health support tools. The study underscores the importance of model selection, balanced datasets, and feature engineering to improve classification accuracy. Future work includes developing AI-driven chatbots for real-time emotion detection and integrating multimodal data to enhance interpretability. This research contributes to advancing automated solutions for mental health care, offering new pathways for timely and personalized interventions.
Application of Deep Learning Algorithm for Web Shell Detection in Web Application Security System Yuranda, Rezky; Negara, Edi Surya
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2234

Abstract

A web shell is a script executed on a web server, often used by hackers to gain control over an infected server. Detecting web shells is challenging due to their complex behavior patterns. This research focuses on using a deep learning approach to detect web shells on the ISB Atma Luhur web server, aiming to develop a model capable of precise detection. By training the model with labeled PHP files, malicious web shells are distinguished from benign files. The study is crucial for enhancing the server's security, preventing hacker attacks, and safeguarding sensitive data. Through preprocessing techniques such as opcode extraction and feature selection, useful pattern recognition for web shell detection is achieved. Training deep learning models like CNN and RNN with LSTM on processed data leads to accuracy evaluation using classification metrics. The CNN model demonstrates superior performance in detection, emphasizing the effectiveness of deep learning for web shell detection. The research contributes to enhancing security in web-based applications, protecting against cyber threats like web shells.
ANALISIS KEAMANAN INFORMASI DAN TATA KELOLA SISTEM DI SEKOLAH TINGGI ILMU KESEHATAN BINA HUSADA MENGGUNAKAN INDEKS-KAMI evariani, evariani; Herdiansyah, M. Izman; Negara, Edi Surya; Sutabri, Tata
Djtechno: Jurnal Teknologi Informasi Vol 6, No 1 (2025): April
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i1.6126

Abstract

Integrasi sistem elektronik di institusi pendidikan sangat penting untuk meningkatkan efisiensi operasional dan memastikan keamanan informasi. Namun, STIK Bina Husada menunjukkan tingkat penggunaan sistem elektronik yang rendah, sehingga menimbulkan kekhawatiran tentang proses operasional dan kematangan keamanan informasinya. Studi ini bertujuan untuk menilai kondisi terkini penggunaan sistem elektronik dan kematangan keamanan informasi di STIK Bina Husada, mengidentifikasi kesenjangan, dan mengusulkan rekomendasi yang dapat ditindaklanjuti untuk meningkatkan kedua area tersebut. Desain penelitian metode campuran digunakan, melibatkan penilaian kualitatif terhadap penggunaan sistem elektronik dan tingkat kematangan keamanan informasi. Partisipan termasuk staf administrasi dan personel TI di STIK Bina Husada, dengan konteks penelitian berada dalam kerangka operasional institusi. Evaluasi mengungkapkan skor penggunaan sistem elektronik sebesar 12 dari 50, menunjukkan kebutuhan peningkatan yang signifikan. Tingkat kematangan keamanan informasi juga rendah, dengan total skor 417, mengkategorikannya pada tingkat kematangan I+ dan II. Area spesifik yang memerlukan perhatian mencakup tata kelola, manajemen risiko, manajemen aset, dan keamanan teknologi. Temuan ini menyoroti kebutuhan mendesak bagi STIK Bina Husada untuk mengembangkan kebijakan keamanan informasi yang komprehensif, meningkatkan praktik manajemen risiko, dan menerapkan struktur tata kelola yang efektif untuk mencapai tingkat kematangan target III+ (3,5), yang diperlukan untuk kesiapan sertifikasi ISO 27001.
COMPARING DEEP LEARNING AND MACHINE LEARNING FOR DETECTING FAKE NEWS ON SOCIAL MEDIA Ria Andryani; Dedek Julian; Rezki Syaputra; Ahmad Syazili; Ahmad Rusli; Rahmat Ramadan; Edi Surya Negara
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 9 No. 3 (2025): Volume 9, Nomor 3, September 2025
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v9i3.46370

Abstract

One of the critical issues resulting from the increasing penetration of social media is the spread of fake news. This can damage public information and influence mass opinion, leading to conflict. To overcome this problem, machine learning and deep learning-based approaches have been continuously developed to detect fake news on various social media platforms automatically. This article aims to compare the effectiveness of these two approaches in detecting fake news. The methods used include the implementation of traditional machine learning algorithms, such as Support Vector Machines (SVM) and Random Forest, as well as deep learning-based approaches, including Long Short-Term Memory and Self-Organizing Maps. Datasets containing real and fake news from various social media sources are used to train and evaluate these models. Model performance is measured based on accuracy, precision, recall, and F1-score. This study aims to determine which approach is more effective and identify challenges in implementing these algorithms in a dynamic social media environment. The results obtained show that the Random Forest algorithm achieves an accuracy level of 100%, surpassing other algorithms, including Long Short-Term Memory with an F-1 Score of 97%, Self-Organizing Map with an F-1 Score of 96%, and Support Vector Machine with an F-1 Score of 92%.
EARLY DETECTION OF ACADEMIC DEPRESSION USING SMARTPHONE-BASED MACHINE LEARNING MODELS Edi Surya Negara; Latius Hermawan; Hastari Mayrita; Desy Arisandy; Mohamad Farozi; Rahmat Ramadan; Sunda Ariana; Ria Andryani
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 9 No. 3 (2025): Volume 9, Nomor 3, September 2025
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v9i3.46375

Abstract

Mental health in developing countries is a common and complex problem. The problem continues to increase and is closely related to low self-confidence, negative interpersonal relationships, and academic depression. This can affect students' ability to complete academic assignments on a university scale. An AI-based early detection application can potentially improve mental health services related to treatment access. This system can help identify users who may be depressed based on the language used, especially for those who are reluctant to seek professional solutions due to the negative stigma of mental health. This study uses a qualitative descriptive method involving observation, in-depth analysis of group conversations, and early detection of academic depression by identifying conversation patterns between students and counselors as the basis for developing a smartphone-based application. This study produced a dataset of 395 depression-level data entries used as training data to develop a machine-learning model. A prototype of an academic depression detection application has been developed as a mobile-based application.
Co-Authors AA Sudharmawan, AA Adam Prasetya Ade Putra Adi Wijaya Adila, Nia Aditya, Ferdi Agam, Padel Mohammad Ahmad Ghiffari Ahmad Rusli Ahmad Syazili Akhiruddin, Deddy Rezano Amanda, Riyan Amin, Zulius Akbar Andreean Dharma Arisandi Andry Meylani andryani, ade Andryani, Ria Andryani, Ria Ari Hardiyantoro Susanto Arjun, Jennifer Axel Natanael Salim Azhiman, Fauzan Bhagaskara, - Bhianta Wijaya Chairul Mukmin Damayanti, Nita Rosa damayanti, selvia Dasmen, Rahmat Novrianda Deddy Rezano Akhiruddin Dedek Julian Dedi Irawan Dedy Syamsuar Dedy Syamsuar Dendi Triadi Dendi Triadi Deni Erlansyah Deris Stiawan Destarina, Nova Desy Arisandy Diana Donan, Hendri ENDRI ENDRI ERIENE DHEANDA evariani, evariani Fajarino, Aldo Fatoni Ferdiansyah Ferdiansyah Fernandy Jupiter Firdaus Firdaus Hastari Mayrita Hendra Marta Yudha Herdiansyah, Izman Herdiansyah, M. Izman Herdiansyah, M. Izman Indah, Mayang Puspa Jepri Yandi Juminovario Juminovario Junisti, Alfina Wulan KENI KENI Kiki Rizky Nova Wardani Kisworo, Marsudi Wahyu Kurniawan, Tri Basuki Latius Hermawan Linda Atika Linda Atika Liza Fahreni M Izman Herdiansyah Maria Ulfa Meilinda Meilinda Mery Sintia Mochammad Imron Awalludin Mohamad Farozi Muhamad Akbar Muhammad Cahyono MUHAMMAD FAHMI Muhammad Izman Herdiansyah Muhammad Izman Herdiansyah Muhammad Marzuki Muhammad Marzuki Muhammad Qurhanul Rizqie Muhammad Raihan Muhammad Wahyudi Nanda Tri Haryati Nico Michael Bryan Novaria Kunang, Yesi Novita Anggraini Novrianda, Rahmat Nurhachita Nurhachita Nurhachita Nurhachita Oktariansyah Oktariansyah, Oktariansyah Pratiwi, Ayu Okta Prihambodo Hendro Saksono Purnama Dharmawan Puspita Dewi Setyadi Putra, Yusuf Andi Putri Armilia Prayesy Qisthiano, M Riski Rahmad Kartolo Rahmat Gernowo Rahmat Ramadan Rahmat Ramadan Raihan, Muhammad Ramadani Ramayanti, Indri Rasmila, Rasmila REZA PAHLEVI Reza Pahlevi Reza Vidi Aditama Rezki Syaputra Ria Andriani Ria Andryani Rianda, M. Rianda Rifan Fadilah Rivaldi, Ahmad Riyan Amanda Rizma Adlia Syakurah RR. Ella Evrita Hestiandari Saksono, Prihambodo Hendro Sari, Yulia Permata Saro, Dewi Novita Sunda Ariana, Sunda Supratman, Edi Suryayusra Syaputra, Rezki Tata Sutabri Tri Basuki Kurniawan Triadi , Dendi Triyunsari, Desra Usman Ependi Wawan Setiawan Widya Cholil Winata Nugraha Winoto Chandra Yeni Widyanti Yepi Kusmeta Yesi Novaria Kunang Yessi Novaria Kunang Yuni Amrina Yuranda, Rezky Yusuf Andi Putra Yusuf, Abi daud Zulius Akbar Amin