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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Teknika Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Jurnal Teknologi Informasi dan Ilmu Komputer Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) International conference on Information Technology and Business (ICITB) jurnal Teknologi Informasi Magister Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) JMM (Jurnal Masyarakat Mandiri) Prosiding Seminar Nasional Darmajaya JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Mathvision : Jurnal Matematika Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat Yumary: Jurnal Pengabdian kepada Masyarakat Journal of Applied Data Sciences Jurnal Abdimas Bina Bangsa JUSTIN (Jurnal Sistem dan Teknologi Informasi) Prosiding Konferensi Nasional PKM-CSR Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Publika Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Journal of Data Science Methods and Applications
Hendra Kurniawan
Institut Informatika & Bisnis Darmajaya
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Prediksi Kekambuhan Kanker Tiroid Menggunakan Algoritma Random Forest Safitri, Egi; Rofianto, Dani; Karnila, Sri; Nurjoko, Nurjoko; Kurniawan, Hendra; Arkhiansyah, Yuni; Rizal, Ruki
Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 8 No. 3 (2025): Volume VIII - Nomor 3 - Mei 2025
Publisher : Teknik Informatika, Sistem Informasi dan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/siskom-kb.v8i3.833

Abstract

Kekambuhan kanker tiroid pasca terapi Radioactive Iodine (RAI) merupakan tantangan penting dalam penatalaksanaan jangka panjang pasien. Penelitian ini bertujuan membangun model prediktif untuk mengidentifikasi potensi kekambuhan dengan memanfaatkan data klinis dan patologis menggunakan algoritma Random Forest. Dataset terdiri atas 383 data pasien dengan 13 atribut, termasuk usia, jenis kelamin, staging kanker, jenis patologi, klasifikasi risiko, dan respons terhadap terapi. Proses pra-pemrosesan meliputi penyandian data kategorik, eksplorasi fitur, dan pembagian data latih dan uji secara stratifikasi. Hasil evaluasi menunjukkan performa tinggi dari model, dengan akurasi 96,5%, presisi 96,7%, recall 90,6%, dan AUC 0,99. Analisis fitur menggunakan SHAP mengungkap bahwa Stage, Response, dan Risk merupakan faktor paling berkontribusi terhadap prediksi kekambuhan. Penelitian ini menunjukkan bahwa model Random Forest tidak hanya efektif dalam klasifikasi biner, tetapi juga dapat diinterpretasikan secara klinis untuk mendukung pengambilan keputusan medis yang lebih personal dan preventif.
Progressive Massive Fibrosis Detection Using Generative Adversarial Networks and Long Short-Term Memory Irianto, Suhendro Y.; Karnila, Sri; Hasibuan, M.S.; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Kurniawan, Hendra
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.707

Abstract

Contribution: Progressive Massive Fibrosis (PMF) is a severe form of pneumoconiosis, affecting individuals exposed to mineral dust, such as coal miners and workers in the artificial stone industry. This condition causes significant pulmonary impairment and increased mortality. Early and accurate detection is vital for effective management, yet traditional diagnostic methods face challenges in differentiating PMF from other pulmonary diseases due to variability in clinical presentations and limitations in imaging techniques. Idea: The study introduces a novel diagnostic framework that integrates Generative Adversarial Networks (GAN) and Long Short-Term Memory (LSTM) networks to enhance the detection and monitoring of PMF. The GAN generates high-fidelity synthetic imaging data to address the issue of limited datasets, while the LSTM network captures temporal patterns in patient data, enabling real-time monitoring of disease progression. Objective: The primary objective of this research is to develop an AI-driven model that improves the accuracy and efficiency of PMF detection and monitoring, facilitating early diagnosis and better treatment planning. Findings: The integrated GAN-LSTM model significantly outperformed traditional diagnostic methods. It proved high accuracy, a Dice coefficient of 0.85, and an Area Under the Curve (AUC) of 0.92, showing precise differentiation of PMF from other pulmonary conditions, such as lung cancer and tuberculosis. Results: The GAN-LSTM framework achieved an accuracy of 91.3%, suggesting that the fusion of GAN and LSTM technologies can effectively address the challenges of limited datasets and heterogeneous disease progression. The model showed promise in enhancing the non-invasive detection and ongoing monitoring of PMF. Novelty: This research stands for a significant advancement in PMF diagnostics by combining GAN and LSTM technologies in a single framework. This approach improves diagnostic accuracy and eases continuous disease monitoring, offering a non-invasive and highly precise solution for PMF detection.
Prediksi Keberhasilan Akademik Menggunakan Metode Regressi Logistik Dan Support Vector Machine Triyasri, Novita; Safitri, Egi; Kurniawan, Hendra; Saputra, M Hardi; Syidada, Amran Rahman; Pratama, Raynaldo Syah
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 13, No 4 (2025)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v13i4.89731

Abstract

Penelitian ini bertujuan untuk memprediksi keberhasilan akademik dengan menggunakan dua metode yaitu regresi logistik dan support vector machine (SVM). Keberhasilan akademik seringkali dipengaruhi oleh banyak faktor, antara lain motivasi siswa, keterampilan belajar, dan kondisi sosial ekonomi. Oleh karena itu penting untuk mengidentifikasi variabel-variabel yang mempengaruhi keberhasilan akademik dan menggunakan teknik analisis yang tepat untuk menghasilkan prediksi yang akurat. Data yang digunakan di Penelitian ini mencakup variabel-variabel seperti nilai ujian, motivasi belajar dan tingkat kehadiran siswa. Metode regresi logistik digunakan untuk menganalisis hubungan antara variabel independen dan variabel dependen (hasil akademik), sedangkan SVM digunakan untuk mengklasifikasikan siswa berdasarkan prestasi akademiknya. Hasil penelitian menunjukkan bahwa kedua metode tersebut memberikan tingkat akurasi yang signifikan dalam memprediksi keberhasilan akademik siswa. Namun Regresi logistik menghasilkan model yang lebih sederhana, SVM menunjukkan keunggulan dalam hal akurasi dan kemampuan mengklasifikasikan siswa dengan prestasi akademik lebih tinggi. Penelitian ini memberikan informasi berharga bagi para pendidik dan manajer pendidikan untuk mengidentifikasi siswa yang memerlukan perhatian lebih dalam pembelajaran dan merancang intervensi yang lebih efektif untuk meningkatkan hasil akademik siswa.
Komparasi Metode Apriori dan FP-Growth Data Mining Untuk Mengetahui Pola Penjualan Purwati, Neni; Pedliyansah, Yogi; Kurniawan, Hendra; Karnila, Sri; Herwanto, Riko
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.4876

Abstract

 Sales data is generally still rarely used, as well as the Perfume Corner shop just piling up in the database, even though there are problems experienced by the store regarding sales data for the best-selling products and to increase the number of sales of subsequent perfume products, so that the store can survive and develop even better. The algorithm that can be used to manage sales data to overcome this problem is Apriori. The research method used in this research is the KDD (Knowledge Discovery in Database) process. This research produces a high frequency pattern for itemsets with a minimum support value of 20% resulting in products that become The Most Tree Items namely Jo Malone 82.49%, Zarra 28.25%, and Zwitsal 20.34%. While the association rules formed from the value of Min. Supp 20% and Min. Conf 80%, get a combination of 2 itemsets, namely Jo Malone and Zarra. Whereas for the combination of 3 itemsets, namely Jo Malone, Zarra and Baccarte with valid and strong status, it is proven by a lift value greater than 1, therefore the association rules are very appropriate to be used.
Assessment of Usability and Acceptance of An Academic Information System Using SUS And TAM Adaptation Nurlistiani, Rini; Romadona, Romadona; Kurniawan, Hendra; Nursiyanto, Nursiyanto
Prosiding International conference on Information Technology and Business (ICITB) 2023: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 9
Publisher : Proceeding International Conference on Information Technology and Business

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

Abstract

Organizations, companies, and the world of education carry out all learning activities using e-learning. There is an important part that requires an academic system with structured data, namely the system at private universities in Indonesia, for example,Informatics and Business Institute Darmajaya. Darmajaya is one of the educational institutes that uses online learning media information technology called e-learning for students and lecturers. The newest information system used at IIB Darmajaya is the academic information system (AIS) which consists of Darmajaya students and lecturers. Result from the assessment showing of lecturers understand how to use AIS with value 56.92, and 65.93 from students of IIB Darmajaya. Keywords :SUS,TAM, Evaluation, Acceptance, Usability
OPTIMALISASI PENANGANAN SPARSITY MENGGUNAKAN RANDOM FOREST, DEEP LEANING, DAN HOT-DECK IMPUTATION Lestari, Sri; Satrio, Rafli Banu; Kurniawan, Hendra; Saleh, Sushanty
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7692

Abstract

Sparsity data dalam sistem rekomendasi dapat menurunkan akurasi prediksi dan relevansi saran. Penelitian ini membandingkan tiga metode imputasi—Random Forest Imputation, Deep Learning-Based Imputa-tion, dan Hot-Deck Imputation—dengan evaluasi menggunakan RMSE pada berbagai tingkat sparsitas. Hasil menunjukkan bahwa Random Forest Imputation consistently menghasilkan RMSE terendah di semua kondisi. Pada sparsitas 20%, metode ini lebih unggul dibandingkan Deep Learning-Based Imputation dengan selisih hingga 0.443 dan Hot-Deck Imputation hingga 0.338. Perbedaan RMSE se-makin meningkat seiring bertambahnya sparsitas, dengan selisih terbesar pada sparsitas tertinggi masing-masing dataset. Secara kese-luruhan, Random Forest Imputation terbukti paling efektif dalam me-nangani sparsitas dan meningkatkan akurasi rekomendasi.
Hybrid Recommendation System Based on Implicit Feedback with Collaborative Filtering and Gradient Boosting Kurniawan, Hendra; Zahra, Amalia
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 2 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112368

Abstract

Recommendation systems are essential components in video streaming services as they assist users in selecting relevant content in line with the increasing availability of large-scale content. However, most recommendation systems still rely on explicit feedback data such as ratings, which are often unavailable on many platforms. This study aims to develop a hybrid recommendation system based on implicit feedback by constructing an interaction score derived from user behavior as a substitute for ratings. The proposed model integrates collaborative filtering methods (matrix factorization and k-nearest neighbor) with the CatBoost gradient boosting decision tree algorithm. The evaluation was conducted using empirical data from a video streaming service, with performance measured using root mean squared error (RMSE) and mean absolute error (MAE). The results indicate that the hybrid model achieves lower RMSE and MAE values compared to individual models. These findings confirm that the hybrid approach is effective in improving recommendation accuracy while also contributing to enhanced user experience quality in video streaming platforms without explicit rating data.
Implementasi Deep Learning Algoritma Convolutional Neural Network untuk Klasifikasi Kesegaran Buah dan Sayur Latifa, Annisa; Hikmah, Nor; Kurniawan, Hendra; Rohmat Hidayat, Kardilah; Larasati, Niken; Rumini
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 2: April 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Buah dan sayur merupakan sumber utama vitamin, mineral, dan serat yang sangat penting untuk menjaga kesehatan tubuh. WHO merekomendasikan konsumsi sebesar 400 gram per hari untuk gizi seimbang. Namun, kualitas dan kesegaran buah dan sayur sering kali sulit diidentifikasikan secara manual, terutama dalam skala besar, karena metode tradisional memiliki keterbatasan akurasi dan rentan terhadap kesalahan manusia. Kemajuan kecerdasan buatan, khususnya deep learning, memberikan solusi inovatif dalam klasifikasi citra. Convolutional Neural Network (CNN), telah terbukti efektif dalam pengenalan dan klasifikasi gambar. Penelitian ini menerapkan CNN dengan arsitektur Inception V3 dalam mengklasifikasikan kesegaran buah dan sayuran menjadi dua kategori utama, yaitu segar dan busuk. Model dikembangkan menggunakan dataset yang terdiri dari 11. 441 citra yang gambar, yang dibagi ke dalam tiga subset utama, yaitu data latih (±44.38%), data validasi (±11.07%), dan data uji (±44.55%). Dengan data kelas terbagi 14 kelas. Hasil penelitian  dengan menggunakan confusion matric  nilai accuracy sebesar 95%  dan hasil evaluasi validation accuracy  sebesar 100% pada epoch ke-4, dengan val_loss terendah sebesar 0.0260  serta nilai MAE  0.26, yang artinya model memiliki kinerja yang sangat baik  dalam mendekteksi kesegaran  buah dan sayur. Penelitian lanjutan disarankan untuk meningkatkan generalisasi model dengan menggunakan dataset yang lebih beragam, dan mengintegrasikan komputasi tepi (edge computing) untuk inspeksi kualitas langsung di Lokasi.   Abstract Fruits and vegetables are primary sources of vitamins, minerals, and fiber, which are essential for maintaining a healthy body. The World Health Organization (WHO) recommends a daily intake of 400 grams for a balanced diet. However, the quality and freshness of fruits and vegetables are often difficult to identify manually, especially at large scale, as traditional methods have limitations in accuracy and are prone to human error. Advances in artificial intelligence, particularly deep learning, offer innovative solutions in image classification. Convolutional Neural Networks (CNNs) have proven effective in image recognition and classification tasks. This study implements a CNN using the Inception V3 architecture to classify the freshness of fruits and vegetables into two main categories: fresh and rotten. The model was developed using a dataset consisting of 11,441 images, divided into three main subsets: training data (approximately 44.38%), validation data (approximately 11.07%), and test data (approximately 44.55%), with 14 distinct classes. The results of the study, based on the confusion matrix, show an accuracy of 95%, and a validation accuracy of 100% at the 4th epoch, with the lowest validation loss recorded at 0.0260 and a MAE of 0.26. These results indicate that the model performs very well in detecting the freshness of fruits and vegetables. Further research is recommended to improve model generalization using more diverse datasets and to integrate edge computing for on-site quality inspection.
SiMoI New Method to Solve the Sparsity Problem in Collaborative Filtering Hendra Kurniawan; Sri Lestari; Sushanty Saleh; Rafli Banu Satrio
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1015

Abstract

Sparsity data is a major challenge in collaborative recommendation systems, characterized by the predominance of missing values within the user-item matrix. When a substantial portion of data is unavailable, the estimation process becomes hindered, and prediction accuracy declines due to limited usable information. To address this issue, this study introduces a novel method called SiMoI (Similarity, Mode, and Minimum Imputation), which is adaptively designed to handle high levels of sparsity. The SiMoI method combines user similarity with imputation strategies based on mode and minimum values. By leveraging subsets of the most informative users and items, the method efficiently fills missing entries while maintaining prediction stability. Evaluation was conducted using both real and synthetic datasets with varying sizes and degrees of sparsity, including an extreme scenario with 93.7% missing data. Experimental results show that SiMoI consistently produces more accurate predictions than baseline methods. Under high-sparsity conditions, SiMoI achieved an RMSE as low as 0.823, outperforming KNNI (0.947) and MEAN (1.021). Moreover, SiMoI demonstrated resilience across different data scales and sparsity distributions, indicating its flexibility and scalability in diverse contexts. These findings suggest that SiMoI is an effective and stable approach for addressing sparsity and holds strong potential for implementation in user-based recommendation systems, particularly in real-world scenarios where data availability is frequently limited.
SISTEM INFORMASI E-COMMERCE TOKO HIJAB BERBASIS WEB DENGAN METODE EXTREME PROGRAMMING Nurlistiani, Rini; Kurniawan, Hendra; Yuliawati, Dona; Maria, Okta
Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Vol. 7 No. 1 (2024): Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data)
Publisher : LP2M Institut Informatika Dan Bisnis Darmajaya

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

Abstract

Development of increasingly modern technological devices can provide advantages for entrepreneurs to market services and products with the aim of expanding market share with a wide reach. The scope of product sales in an area becomes ineffective in being able to compete to attract consumer interest. The role of information technology can be carried out using internet media so that consumers can access it online or can be called e-commerce. The object of this research was carried out at a hijab shop in the Natar area, South Lampung. The problems that exist include the sales process which is carried out directly, such as consumers coming to the shop to buy products, which has an impact on operational costs, energy and time, especially consumers who are in areas where they cannot see information on the availability of the product they want to buy. The system development method used is Extreme Programming. The results obtained are that the use of the website is running well, as evidenced by the results of system testing using blackbox testing of 91.66%, which means that this e-commerce information system is running successfully and can be used by consumers, especially in the South Lampung area and its surroundings.
Co-Authors - Nurjoko Abdi Darmawan Abdullah Merjani, Abdullah Ade Moussadecq Adytama, Muhammad Rezky Agung Pradana Agus Rahardi Ahmad Nur Hakim Amrullah Ahnaf Ronaldo Nadhir Alda Caesar Valensia Alendra Natuah Maensya Andini, Rekha Aprilia Anita Dewi Purwati Annisa Anggun P Annisa Latifa Antoni Suseno Antonio, Yandi Jaya Assatulaini Assatulaini Azima, Muhammad Fauzan Azima, Muhammad Fauzan Bagus Prihadi Damayanti, Irah Danang Ade Muktiawan Dani Rofianto Denny Andreas Desi Ratna Sari Dewi, Deshinta Arrova Dina Warsahanda Dona Yuliawati Edi Edi Pranyoto Egi Safitri Elsa Agustin Marbun Fikri, Ruki Rizal Nul Fitria - Gusnanda Oscar Halimah Halimah Harijanto Wijaya Hasibuan, M.S. Hermanto HERMANTO Herwanto, Riko Herwanto, Riko Hikmah, Nor Irianto, Suhendro Y. Kultsum, Rahil Urwa Kurniawan, Tri Basuki Lilik Joko Susanto M Yusendra M. Zaky Fanany Zaky Maria, Okta Melda Agarina Mochammad Imron Awalludin Muhamad Ariza Eka Yusendra Muhamad Iqbal Ardiansyah Muhammad Ariza Eka Yusendra Muhammad Redintan Justin Muhammad Rezky Adytama Muhammad Sahri Muji Lestari Neni Purwati Niken Larasati Novi Herawadi Sudibyo Nurjoko Nurjoko Nurjoko Nurjoko Nurlistiani, Rini Nursiyanto Pedliyansah, Yogi Prasetyo, Indra Pratama, Raynaldo Syah Pratama, Reza Lintang Hana Raden Abdurrahman Rafli Banu Satrio Raihan Hasbid Rini Nurlistiani Rizal, Ruki Rizki Aditya Ramadhan Rizky Samjaya Putra Rohiman, Rohiman Rohmat Hidayat, Kardilah Romadona, Romadona Rossa Wulandari Ruki Rizal Ruki Rizal Ruki Rizalnul Fikri Rumini Safitri, Egi Saputra, M Hardi Sasya Nadira Satrio, Rafli Banu Shofiyurrahman Shofiyurrahman Siswahyudianto Soleh, Ary Sofyan Sri Karnila Sri Karnila Sri Karnila Sri Karnila Sri Karnila Sri Karnila Karnila Sri Lestari Sri Rahayu Stefanus Rumangkit Suhendro Yusuf Irianto Sumarya, Edi Supriyadi Susanti Susanti Susanti Susanti Susanto, Lilik Joko Sushanty Saleh Sutedi Sutedi Syahputra, Lingga Syidada, Amran Rahman Theresia, Sumini Tri Erri Astoeti Tri Melda Yama Triyasri, Novita Wicakso Bandung Bondowoso Widijanto Sudhana Y, M Ariza Eka Y. Suhendro Yan Aditiya Pratama Yogi Pedliyansah Yuda Septiawan Yuni Arkhiansyah Yusminar Yusminar Yusminar Yusminar Zahra Putri Assyfa Zahra, Amalia