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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JUSIFO : Jurnal Sistem Informasi Jurnal Informatika Global Journal of Information Systems and Informatics Indonesian Journal of Data and Science Jurnal Digital Teknologi Informasi Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Computer and Information Systems Ampera Journal of Software Engineering Ampera Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Jurnal Software Engineering and Computational Intelligence Prosiding Seminar Nasional Unimus International Journal of Artificial Intelligence in Medical Issues
Dian Hafidh Zulfikar
Universitas Islam Negeri Raden Fatah Palembang
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Health Professions Mathematics Medicine & Pharmacology Nursing Public Health

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Analisis Keberhasilan Sistem Informasi Akademik Universitas Baturaja Menggunakan Human Organization Technology Fit Model Ayu Jayanti; Dian Hafidh Zulfikar; Fathiyah Nopriani
Journal of Software Engineering Ampera Vol. 4 No. 1 (2023): Journal of Software Engineering Ampera
Publisher : APTIKOM SUMSEL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalsea.v4i1.378

Abstract

Academic Information System (SIAKAD) is a system implemented by Baturaja University as a support for lecture activities. This study aims to determine what factors influence the success of SIAKAD using the Human Organization Technology Fit Model by applying 8 variables, namely system quality, information quality, service quality, system use, user satisfaction, structure, Environment and net benefit. Data collection was carried out by distributing questionnaires to students and lecturers of Baturaja University. Sampling using Probability Sampling with Proportionate Stratified Random Sampling technique. The data analysis used is PLS-SEM using SmartPLS 3.0 software. Based on the results of hypothesis testing of all variables, it was found that the factors that influence the success of SIAKAD are service quality 7.808, user satisfaction 8.389, have an influence on system use. Information quality 3.046 service quality 7.326 has an influence on user satisfaction. Information quality 2,605 service quality 3,671 has an influence on structure. System use 2.98 structure 4.223 environment 7.875 has an influence on net benefits and structure 9.627 has an influence on the environment. And factors that do not affect the success of SIAKAD, namely system quality 1.103 has no effect on system use, system quality 1.167 has no effect on user satisfaction, system quality 1.304 has no effect on structure, information quality 0.394 has no effect on system use and user satisfaction 0.787 has no effect on the net benefit.
Analisis Tingkat Kepuasan Pengguna Pada Aplikasi Easy Access PT. Semen Baturaja (Persero) Tbk Menggunakan Metode End User Computing Satisfaction (EUCS) Putri Ananda Destary; Dian Hafidh Zulfikar
Prosiding Seminar Nasional Unimus Vol 6 (2023): Membangun Tatanan Sosial di Era Revolusi Industri 4.0 dalam Menunjang Pencapaian Susta
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Aplikasi Easy Access merupakan aplikasi absensi yang digunakan para anggota PT. Semen Baturaja (Persero) Tbk khususnya pada Kantor Cabang Kota Palembang. Aplikasi Easy Access yang digunakan oleh PT. Semen Baturaja telah dirancang untuk mencatat kehadiran karyawan, mengelola cuti, izin, lembur dan memantau jam kerja yang dapat diakses semua karyawan dengan akun mereka masing-masing. Dalam implementasi Easy Access diperlukan sebuah evaluasi terhadap aspek kenyamanan dan kepuasan bagi para pengguna aplikasi. Dalam evaluasi kepuasan pengguna pada sebuah system ataupun aplikasi memberikan dampak yang baik bagi Perusahaan dan juga pengguna. Perusahaan dapat menemukan peluang utukmeningkatkan fitur aplikasi yang relevan dan lebih baik lagi. Oleh karena itu aplikasi Easy Access perlu dilakukan identifikasi masalah untuk mengetahui seberapa puas pengguna terhadap aplikasi Easy Access. Metode yang digunakan dalam penelitiaan ini adalah metode analisis End User Computing Satisfaction (EUCS) yang dimana memfokuskan evaluasi tingkat kepuasan pengguna akhir menggunakan dimensicontent, accuracy, format, ease of use, dan timeliness. Pengumpulan data pada pennelitian ini dilakukan dengan menyebar kuesioner kepada 75 responden yaitu anggota PT. Semen Baturaja Persero Tbk. Teknik pengambilan sampel menggunakan random sampling. Pada hasil olah data kuesioner didapatkan hasil perhitungan persentase pada tiap-tiap dimensi EUCS variabel content sebesar 90,2% yang artinya sangat puas, variabel accuracy sebesar 89,2% sangat puas, variabel format 90,4% sangat puas, variabel ease of use 89,6% sangat puas, dan variabel timeliness 89,4% sangat puas. Berdasarkan hasil persentase pada tiaptiap dimensi telah menunjukkanbahwa aplikasi Easy Acces sudah merasa puas.Kata Kunci : Aplikasi, Absensi, Kepuasan Pengguna, EUCS.
Advancements in Agricultural Automation: SVM Classifier with Hu Moments for Vegetable Identification Waluyo Poetro, Bagus Satrio; Maria, ⁠⁠Eny; Zein, Hamada; Najwaini, Effan; Zulfikar, Dian Hafidh
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.123

Abstract

This study investigates the application of Support Vector Machine (SVM) classifiers in conjunction with Hu Moments for the purpose of classifying segmented images of vegetables, specifically Broccoli, Cabbage, and Cauliflower. Utilizing a dataset comprising segmented vegetable images, this research employs the Canny method for image segmentation and Hu Moments for feature extraction to prepare the data for classification. Through the implementation of a 5-fold cross-validation technique, the performance of the SVM classifier was thoroughly evaluated, revealing moderate accuracy, precision, recall, and F1-scores across all folds. The findings highlight the classifier's potential in distinguishing between different vegetable types, albeit with identified areas for improvement. This research contributes to the growing field of agricultural automation by demonstrating the feasibility of using SVM classifiers and image processing techniques for the task of vegetable identification. The moderate performance metrics emphasize the need for further optimization in feature extraction and classifier tuning to enhance classification accuracy. Future recommendations include exploring alternative machine learning algorithms, advanced feature extraction methods, and expanding the dataset to improve the classifier's robustness and applicability in agricultural settings. This study lays a foundation for future advancements in automated vegetable sorting and quality control, offering insights that could lead to more efficient agricultural practices.
Performance Comparison of CNN and ResNet50 for Skin Cancer Classification Using U-Net Segmented Images Aris Wahyu Murdiyanto; Zulfikar, Dian Hafidh; Waluyo Poetro, Bagus Satrio; Siregar, Alda Cendekia
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.200

Abstract

Skin cancer is a significant global health issue, with melanoma, basal cell carcinoma, and actinic keratosis being the most common types. Early and accurate detection is critical to improve survival rates and treatment outcomes. This study evaluates the performance of Convolutional Neural Networks (CNN) and ResNet50 in classifying segmented images of skin lesions. The dataset, sourced from Kaggle, was pre-processed using U-Net for lesion segmentation to enhance the quality of input data. Both models were trained and evaluated using accuracy, precision, recall, and F1-score metrics. The CNN model demonstrated a balanced performance across classes, with a weighted F1-score of 47%, but suffered from overfitting, as indicated by the divergence between training and validation losses. ResNet50 achieved better recall for basal cell carcinoma (100%) but failed to classify actinic keratosis and melanoma, resulting in a macro F1-score of 23%. The findings reveal that U-Net segmentation improved classification focus but was insufficient to address dataset imbalance and model-specific limitations. This study highlights the challenges of skin cancer classification using deep learning and underscores the importance of addressing data imbalance and overfitting. Future research should explore advanced techniques, such as ensemble methods, data augmentation, and transfer learning, to improve the generalization and clinical applicability of these models. The proposed framework serves as a foundation for further investigation into automated skin cancer detection systems.
Pengelompokan Daerah Rawan Bencana di Sumatera Selatan Menggunakan Algoritma K-Means Zulfikar, Dian Hafidh; Setapati, Gerry
Jurnal Software Engineering and Computational Intelligence Vol 2 No 02 (2024)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v2i02.4945

Abstract

Indonesia merupakan negara yang terletak di wilayah pertemuan berbagai lempeng tektonik, sehingga banyak daerahnya memiliki tingkat kerawanan tinggi terhadap bencana alam, termasuk di Provinsi Sumatera Selatan. Penelitian ini bertujuan untuk mengelompokkan wilayah-wilayah rawan bencana di Sumatera Selatan dengan menerapkan teknik data mining menggunakan metode clustering. Algoritma yang digunakan dalam pembentukan klaster adalah K-Means, sebuah metode clustering non-hierarkis yang mampu mengelompokkan data berdasarkan tingkat kesamaan. Data bencana yang memiliki karakteristik serupa akan dikelompokkan dalam satu klaster, sedangkan data dengan karakteristik berbeda akan dimasukkan ke klaster lainnya. Hasil penelitian ini menghasilkan pengelompokan daerah rawan bencana ke dalam tiga kategori, yaitu daerah dengan tingkat kerawanan rendah, sedang, dan tinggi. Temuan ini diharapkan dapat menjadi informasi tambahan yang berguna bagi pemerintah dalam upaya penanggulangan bencana di Sumatera Selatan.
Data Hiding menggunakan Play Fair Kriptografi dan Steganografi pada Domain DCT dengan Operasi Logika XOR Zulfikar, Dian Hafidh
Jurnal Software Engineering and Computational Intelligence Vol 3 No 01 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i01.5410

Abstract

Artikel ini membahas strategi untuk menyembunyikan data dengan menggabungkan Teknik kriptografi dan steganografi. Kriptografi digunakan untuk mengubah teks biasa menjadi teks terenkripsi, sementara steganografi digunakan untuk menyembunyikan teks terenkripsi tersebut dalam sebuah gambar. Metode yang diusulkan menggabungkan Play Fair Cipher untuk enkripsi teks dan teknik Discrete Cosine Transform (DCT) serta operasi logika XOR untuk menyembunyikan pesan terenkripsi dalam gambar. Hasilnya menunjukkan tingkat keamanan yang tinggi dan analisis histogram yang mendukung efektivitas sistem ini.  
Predicting Cardiovascular Disease Using Machine Learning: A Feature Engineering and Model Comparison Approa Waluyo Poetro, Bagus Satrio; Zulfikar, Dian Hafidh; Sunia Raharja, I Made; Setiohardjo, Nicodemus Mardanus
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 2 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i2.363

Abstract

Cardiovascular disease (CVD) remains one of the leading causes of mortality globally, emphasizing the need for early detection and effective risk stratification. With the increasing availability of clinical and lifestyle-related health data, machine learning (ML) has become a powerful tool to support data-driven diagnosis and decision-making in healthcare. This study aims to develop and evaluate multiple supervised ML models to predict the presence of cardiovascular disease based on non-invasive features obtained from routine medical checkups. The dataset, comprising 69,301 individual records, includes variables such as age, gender, blood pressure, cholesterol, glucose levels, body measurements, and lifestyle habits. Following comprehensive data cleaning and feature engineering such as the derivation of BMI, Mean Arterial Pressure (MAP), and Pulse Pressure four classifiers were applied: Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM). Model performance was evaluated using metrics including accuracy, precision, recall, F1-score, and ROC-AUC. Among all models tested, the Gradient Boosting Classifier achieved the highest performance, with a ROC-AUC score of 0.8060 and a balanced precision-recall tradeoff, indicating strong discriminatory power. Visualizations such as ROC curves and confusion matrices confirmed the superior capability of Gradient Boosting in differentiating between patients with and without CVD. These findings demonstrate the viability of ML-driven risk assessment models as decision-support tools in clinical settings, potentially aiding in earlier diagnosis and more personalized intervention strategies.
Co-Authors Agus Harjoko Aminullah Imal Alfresi Anggi Sefti Utami Aris Wahyu Murdiyanto Ayu Jayanti Bahriyanto Catur Eri Gunawan Dini Ken Rifarsih Effan Najwaini Fathiyah Nopriani Fenny Purwani Hamada Zein Maria, ⁠⁠Eny Masdalia Julya Pranita Nicodemus Mardanus Setiohardjo Orik Dwi Pebriani Perni Bunga Lestari Poetro, Bagus Satrio Waluyo Putra, Anton Putri Ananda Destary Setapati, Gerry Siregar, Alda Cendekia Sri Rahayu Sunia Raharja, I Made