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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications Seminar Nasional Informatika (SEMNASIF) Jurnal Teknologi dan Sistem Komputer Knowledge Engineering and Data Science JIKO (Jurnal Informatika dan Komputer) Jurnal TAM (Technology Acceptance Model) JURNAL INSTEK (Informatika Sains dan Teknologi) ILKOM Jurnal Ilmiah IJID (International Journal on Informatics for Development) JURIKOM (Jurnal Riset Komputer) ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal E-Komtek JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal of Informatics and Communication Technology (JICT) Konstelasi: Konvergensi Teknologi dan Sistem Informasi Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Scientific Research, Education, and Technology SmartComp Journal of Technology Informatics and Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Indonesian Journal of Computer Science Journal of Informatics and Communication Technology (JICT) Jurnal TAM (Technology Acceptance Model) Jurnal Abdi Rakyat Journal of Engineering, Electrical and Informatics
Enny Itje Sela
Master Program Of Information Technology, Universitas Teknologi Yogyakarta, Jl. Siliwangi (Ringroad Utara), Jombor, Sleman, D.I. Yogyakarta 55285|Universitas Teknologi Yogyakarta
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Nutritional Status Classification Of Stunting In Toddlers Using Naive Bayes Classifier Method Risky Devandra Hartana; Enny Itje Sela
Journal of Technology Informatics and Engineering Vol. 3 No. 1 (2024): April : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i1.154

Abstract

Stunting in toddlers is one of the prevalent issues of malnutrition in Indonesia. The causes of Stunting are diverse, and one contributing factor is the insufficient nutritional intake required for toddlers. The categorization of Stunting nutritional status in toddlers is crucial to identify those experiencing Stunting, enabling appropriate interventions to prevent more serious health problems in the future. This research aims to develop a classification model for short nutritional status in toddlers using the Naive Bayes Classifier method. The data utilized in this study originate from anthropometric measurements of toddlers in the Malebo area, Kandangan, Temanggung, Central Java. The anthropometric data include weight, height, and age of the toddlers. This data is then processed using the Naive Bayes Classifier method to classify the nutritional status of Stunting in toddlers. The results of this research are expected to assist in identifying toddlers experiencing Stunting, facilitating appropriate interventions to prevent more serious health issues in the future. Additionally, the Naive Bayes Classifier method employed can be applied in similar studies to enhance the quality of life, especially for children in Indonesia, particularly in the Malebo area, Kandangan, Temanggung, Central Java.
Penerapan Augmented Reality Pada Aplikasi Pembelajaran Senjata Tradisional Indonesia Berbasis Android. Adidarma, Muhamad Bahru; Sela, Enny Itje
KONSTELASI: Konvergensi Teknologi dan Sistem Informasi Vol. 4 No. 2 (2024): Desember 2024
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/konstelasi.v4i2.10269

Abstract

Indonesia adalah negara yang kaya akan keberagaman suku bangsa, menjadi rumah bagi beragam budaya, bahasa, dan tradisi. Salah satu warisan budaya yang berharga adalah senjata tradisional. Namun, dalam era modern ini, keberadaan serta pemahaman akan senjata tradisional sering terabaikan, terutama di kalangan generasi muda yang lebih tertarik pada teknologi modern. Penelitian ini bertujuan untuk merancang aplikasi edukasi berbasis Augmented Reality (AR) pada platform Android sebagai sarana interaktif untuk memperkenalkan senjata tradisional Indonesia. Data mengenai senjata tradisional dikumpulkan melalui kajian literatur yang mencakup senjata dari setiap provinsi di Indonesia. Aplikasi ini memanfaatkan teknologi AR dengan marker untuk menghasilkan objek 3D, audio, serta informasi terkait senjata tersebut. Fitur kuis juga disertakan sebagai evaluasi dari proses pembelajaran. Pengujian aplikasi dilakukan secara internal untuk memastikan fungsionalitas teknologi AR, dan hasilnya menunjukkan bahwa aplikasi dapat beroperasi dengan baik serta berpotensi menjadi alat edukasi yang menarik di masa depan, terutama bagi generasi muda.
Deteksi Citra Wajah Menggunakan Algoritma Haar Cascade Classifier: Face Detection Using Haar Cascade Classifier Algorithm Nugroho, Faishal Tirto; Sela, Enny Itje
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 1 (2024): MALCOM January 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i1.988

Abstract

Manusia dapat mengenali objek dengan sangat mudah berbeda dengan komputer. Jika komputer ingin mengenali sebuah objek harus dilakukan proses pelatihan yang sangat lama ada banyak sekali metode yang dapat digunakan untuk melatih komputer agar dapat mendeteksi suatu objek dengan baik salah satunya yaitu dengan algoritma haar cascade classifier. Pada penelitian ini akan membawakan topik pendeteksian wajah yang akan dilakukan dengan menggunakan algoritma haar cascade classifier. Algoritma haar cascade classifier sudah menjadi algoritma yang biasa digunakan untuk pendeteksian wajah. Dengan menggunakan algoritma ini dapat melatih suatu sistem komputer agar dapat mendeteksi citra wajah. Untuk melatih sistem agar dapat mendeteksi wajah diperlukan sebuah data berupa wajah. Pada penelitian ini akan menggunakan dataset berupa wajah dan bukan wajah. Setelah melakukan pelatihan dapat dihasilkan suatu sistem yang dapat mendeteksi wajah. Dengan menggunakan OpenCV yang disambungkan ke webcam laptop sistem pendeteksian akan langsung berjalan. Hasilpengujian pada penelitian ini menunjukan wajah dapat terdeteksi dengan baik. Wajah yang terdeteksi tidak hanya wajah yang menghadap kedepan kamera saja akan tetapi wajah yang menghadap kesamping kanan, kiri, atas dan bawah juga dapat terdeteksi dengan baik.
Klasifikasi Rimpang Menggunakan Metode K-Nearest Neighbor dan Ekstraksi Ciri Gray Level Co-occurrence Matrix Asep Zainal Alfarizi; Enny Itje Sela
JURNAL FASILKOM Vol. 14 No. 1 (2024): 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.v14i1.6832

Abstract

Rhizome is a modification of plant stems that grow under the soil surface and function as a storage place for food reserves. This plants have internodes that function produce new shoots and roots. Rhizomes are commonly used by people as spices in cooking and herbal medicine. Rhizomes have many types, such as ginger, sand ginger, fingerroot, turmeric, galangal, and curcuma. These types have similarities to each other, such as texture, shape, and color. These similarities can cause problems such as difficulty in identifying the type of rhizome. The solution to this problem is a computer system that can classify the type of rhizomes. The system in this research was built using the K-Nearest Neighbor method and Gray Level Co-occurrence Matrix texture feature extraction. Research data amounted to 500 images with ginger, sand ginger, fingerroot, turmeric, and galangal classes. The stages of this research are data collection, image resizing, conversion to grayscale, GLCM feature extraction, storing the extraction results into dataframe, dividing data into train data and test data, classification with K-NN, and implement GUI to make operation easier. Accuracy results on this system get a value of 74% on test data and 64% on train data with value of K=11.
Implementasi Extreme Learning Machine untuk Pengenalan Jenis Sepatu Triwibowo, Muhammad Ilham; Sela, Enny Itje
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 12, No 4 (2023): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v12i4.5958

Abstract

Sepatu adalah salah satu alas kaki yang sering digunakan oleh masyarakat saat ini. Sepatu belakangan ini bahkan sudah menjadi sangat populer dan menjadi salah satu kebutuhan primer bagi beberapa orang. Beberapa orang awam yang tidak tau tentang jenis-jenis sepatu dan sering kali salah dalam membeli sepatu. Ditambah hal tersebut diperburuk oleh oknum-oknum penjual di online shop yang sering kali memberikan judul barang tidak sesuai dengan produk yang dijual. Extreme Learning Machine merupakan metode pembelajaran baru dari jaringan syaraf tiruan dan salah satu metode dalam Machine Learning. Data yang digunakan pada penelitian ini berupa masing-masing 60 citra sepatu casual, sepatu formal dan sepatu sport untuk data latih. Sedangkan untuk data uji masing-masing 40 citra sepatu casual, sepatu formal dan sepatu sport untuk data latih. Hasil terbaik yang didapat adalah menggunakan 75 neuron dengan akurasi latih 70%, akurasi uji 60%, dan MAPE 27.16.
The Implementation of Artificial Neural Networks for Stock Price Prediction Akbar Maulana; Enny Itje Sela
Journal of Engineering, Electrical and Informatics Vol. 3 No. 3 (2023): Oktober: Journal of Engineering, Electrical and Informatics
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v3i3.2254

Abstract

This research is based on a problem that is difficult to predict stock prices, especially for beginners. Stock prices are hard to predict because they are fluctuating. Users will be easier to predict stock prices through artificial neural networks using Multilayer Perceptron. This MLP is a variant of an artificial neural network and is a development of perceptron. The selection of the Multilayer Perceptron method is based on the ability to solve various problems both classification and regression. The research conducted by the author is a regression problem as the MLP is tasked to predict the close price or closing price of stock after seven days. The results of the model built are able to predict stock prices and produce good accuracy because the resulting RMSE value produced 0.042649862994352014, which is close to 0. Keywords: Machine Learning, Stock Price Prediction, Neural Network, Multilayer Perceptron, MLP.
Implementasi Face Recognition Untuk Sistem Presensi Universitas Menggunakan Convolutional Neural Network Syahrul Gunawan Ramdhani; Enny Itje Sela
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3498

Abstract

Penerapan kecerdasan buatan (AI) dalam teknologi pengenalan wajah bertujuan untuk mengidentifikasi wajah individu yang terdaftar dalam database. Dalam dunia pendidikan, mengelola data kehadiran merupakan hal yang penting untuk evaluasi mahasiswa. Namun, banyak universitas yang masih melakukan pencatatan kehadiran secara manual yang dinilai kurang efektif dan kurang terorganisir. Oleh karena itu, diperlukan model pendeteksi wajah atau pengenalan wajah untuk mengatasi masalah tersebut. Dengan menggunakan face recognition, mahasiswa dapat melakukan absensi hanya dengan memindai wajahnya menggunakan kamera. Data absensi akan langsung terhubung dengan database dan meminimalisir waktu absensi. Dalam penelitian ini, Convolutional Neural Network (CNN) digunakan untuk klasifikasi data wajah mahasiswa. Output dari penelitian ini adalah pengembangan sistem absensi berbasis pengenalan wajah.
Decision Tree C4.5 Performance Improvement using Synthetic Minority Oversampling Technique (SMOTE) and K-Nearest Neighbor for Debtor Eligibility Evaluation Priyanto, Edi; Sela, Enny Itje; Latumakulita, Luther Alexander; Islam, Noourul
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1676.373-381

Abstract

Nowadays, information technology especially machine learning has been used to evaluate the feasibility of debtors. One of the challenges in this classification model is the occurrence of imbalanced datasets, especially in the German Credit Dataset. Another challenge is developing an optimal model for evaluating debtor eligibility. Based on these challenges, this study aims to develop an optimal model for evaluating debtor eligibility on the German Credit Dataset, using the decision trees, k-Nearest Neighbor (k-NN) and Synthetic Minority Oversampling Technique (SMOTE). SMOTE and k-NN is used to overcome challenges regarding imbalanced datasets. While the decision tree are applied to produce a debtor classification model. In general, the steps taken are preparing datasets, pre-processing data, dividing datasets, oversampling with SMOTE, and classification models using decision trees, and testing. Model performance evaluation is represented by accuracy values obtained from the confusion matrix and area under curve (AUC) values generated by the Receiver Operating Characteristic (ROC). Based on the tests that have been carried out, the best accuracy value in the test is obtained at 73.00% and the AUC value is 0.708, in parameters k = 3 and Max-Depth = 25. Based on the analysis produced, the proposed model can improve performance compared to if the dataset is not applied SMOTE.
MODEL DETEKSI DDOS BERBASIS MACHINE LEARNING YANG EFISIEN, INTERPRETABLE, DAN SIAP IMPLEMENTASI OPERASIONAL Andri Yudha Pratama; Khalifatur Rauf; Enny Itje Sela
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 11 No 1 (2026): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v11i1.65922

Abstract

Serangan Distributed Denial of Service (DDoS) menjadi ancaman serius bagi kontinuitas bisnis digital, sehingga membutuhkan sistem deteksi yang akurat, responsif, dan dapat diinterpretasikan. Sebagian besar penelitian terdahulu berfokus pada maksimalisasi akurasi melalui model kompleks, namun kerap mengabaikan efisiensi komputasi dan actionability yang esensial bagi implementasi real-time. Penelitian ini mengevaluasi sembilan skenario deteksi pada dataset CIC-DDoS2019 melalui kombinasi metode seleksi fitur (Pearson, ANOVA, RFE) dan algoritma machine learning (Decision Tree, Random Forest, Logistic Regression). Hasilnya mengungkapkan adanya trade-off signifikan antara kompleksitas model dan latensi deteksi. Penelitian ini mengidentifikasi Skenario E4 (RFE + Decision Tree) sebagai model terbaik berdasarkan trade-off akurasi, latensi, dan memori, dengan recall serangan 0,9999, latensi 900 µs (sekitar 38 kali lebih cepat dari Random Forest), dan efisiensi memori 5.760 Byte. Kontribusi utama penelitian ini mencakup evaluasi multi-objektif yang mengintegrasikan akurasi, latensi, memori, interpretabilitas, dan robustness; pemetaan fitur SHAP ke dalam matriks mitigasi Defense-in-Depth; serta bukti empiris trade-off antara efisiensi operasional dan ketahanan model terhadap serangan adaptif. Analisis SHAP menunjukkan keputusan model didasarkan pada fitur identitas, anomali TCP flag, dan pola idle time. Namun, uji robustness mengindikasikan kerentanan terhadap manipulasi input, menegaskan perlunya strategi mitigasi tambahan dalam kerangka Defense-in-Depth agar model tidak hanya unggul secara statistik, tetapi juga operasional dan adaptif terhadap ancaman cerdas.
Pattern Recognition of Puta Dino Fabric Using Web-Based Convolutional Neural Network Method Luther Alexander Latumakulita; Silviani Esther Rumagit; Hence Beedwel Lumentut; Frangky Jessy Paat; Jaidun Ramadhan Kaplale; Enny Itje Sela
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1103

Abstract

This study aims to develop an intelligent system capable of recognizing traditional woven motifs of Puta Dino, a culturally significant textile from Tidore Island. These motifs are visually complex, poorly documented, and hard for the public to distinguish, highlighting the need for a digital tool to support cultural preservation and accurate identification. This research is the first to build a structured Puta Dino motif database and provide an integrated model designed for real-world use. The approach captured primary images of eight validated motifs and applied systematic preprocessing, including normalization and data augmentation, to enhance variability and strengthen the dataset. A lightweight deep learning model predicated on a convolutional neural network was designed to achieve a compromise between accuracy and computational efficiency. The system was evaluated through cross-validation and independent test data, as well as multiple real-world trials utilizing a web interface. These trials involved different image capture scenarios, including from a distance, moderate distance, close and angled views, and when the fabric surface was folded. The model architecture and system interface with the system are illustrated in the relevant figures, and the tables provide performance data on the system’s training, accuracy in motif classification, and achieved results in real-world conditions. The system demonstrated excellent classification accuracy in controlled test conditions. It showed real-world competency, accurately classifying most motifs in various conditions. The data also point to specific issues with motif recognition in extreme distortion cases, which reflect the typical issues of laboratory-to-field model deployment. The outcomes clearly demonstrate both the possibilities and the limitations of the currently available recognition of culturally significant textiles. The study concludes by exploring the possibilities of expanding the dataset and increasing the depth of learning through more sophisticated techniques, as well as enhancing accessibility to promote sustained community and cultural engagement.
Co-Authors Adidarma, Muhamad Bahru Agus Ardiyanto Agus Ardiyanto Agus Harjoko Agus Harjoko AKBAR MAULANA Akbar, Riolandi Algadrie, Saniah Evatri Alhafiz, Amirrul Dafa Andri Yudha Pratama Anggara, Afwan Anindita Septiarini, Anindita Apriyaldi Lukman Ari Hendrawan Arif Amrulloh Asep Zainal Alfarizi Briyan Efflin Syahputra Edi Priyanto Fatah Nur Saifullah Fathul Am Fathul Am Ferryma Arba Apriansyah Frangky Jessy Paat Ganda Sinarna Hence Beedwel Lumentut Hendrawan, Ari Idham Kholed Rachmawan Islam, Noourul Jaidun Ramadhan Kaplale Julian Ega Prabowo Khalifatur Rauf Luther Latumakulita M Ihsan Munakhir Mudjosemedi Nathaniela Aptanta Parama Noviania, Reski Nugroho, Faishal Tirto Nur Wening Olwin Kirab Novaldy Prawirdani, Adil Rachmawan, Idham Kholed Retantyo Wardoyo Rianto Rianto Risky Devandra Hartana Saucha Diwandari Senoaji, Muhammad Senoaji Wibowo Silviani Esther Rumagit Sri Hartati Steven Ray Sentinuwo Syahrul Gunawan Ramdhani Syamsu Windarti Totok Suprawoto Triwibowo, Muhammad Ilham Tundo, Tundo Utami, Wahyu Sri