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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) 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) Journal of Engineering, Electrical and Informatics (JEEI) 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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Development and Implementation of Mobile Application for Warehouse Inventory Reporting System Julian Ega Prabowo; Sela, Enny Itje
Journal of Informatics and Communication Technology (JICT) Vol. 6 No. 2 (2024)
Publisher : PPM Telkom University

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

Abstract

The company is engaged in the sale of emping mlinjo crackers and currently still uses manual recording methods to manage its operational activities. The main problem faced is the process of making monthly reports to record the movement of goods in and out of goods and monitor the stock of finished goods and raw materials. Along with the rapid growth of the company, this manual method has become inefficient in handling the high volume of goods and many daily transactions. This often leads to errors and discrepancies in reports, resulting in less efficient distribution of goods. This research aims to digitize the warehouse management system of warehouse management by developing a reporting application for the emping warehouse. goods. This application is designed to monitor the movement of goods and generate reports digitally. It is expected that this application will improve operational efficiency by facilitating data recording and management by the SO (Sales Order) Team, Sales Order Team, Production Team, Distribution Team, and provide convenience for the owner as a super admin in managing warehouse activities. The research method used is interviews and observations to identify the problems faced by the company. Based on the findings, the design, development, and implementation of the application and system testing were carried out. Descriptive analysis was used to evaluate the impact of the application on warehouse management and company operations. The results showed that the application was effective in recapitulating incoming and outgoing goods, making it easier to generate reports, and calculating profit or loss from sales transactions. The app enables real-time monitoring, simplifies data input by employees, and increases transaction audit transparency. The digital reporting system simplifies warehouse management, improves operational efficiency, and reduces recording errors and stock management mistakes.
Aplikasi Mobile Untuk Konsultasi Petani Dalam Mendukung Pertanian Digital Alhafiz, Amirrul Dafa; Sela, Enny Itje
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 1 (2025): JPTI - Januari 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.453

Abstract

Pertanian digital merupakan solusi penting dalam meningkatkan produktivitas dan efisiensi sektor pertanian. Aplikasi berbasis mobile telah menjadi sarana inovatif untuk memberikan informasi dan konsultasi kepada petani secara real-time. Penelitian ini bertujuan untuk mengembangkan aplikasi konsultasi berbasis mobile yang dirancang khusus bagi petani, dengan menyediakan layanan konsultasi mengenai teknik pertanian, pemilihan pupuk, pengendalian hama, dan pemantauan cuaca. Aplikasi ini dilengkapi dengan fitur interaktif yang memungkinkan petani untuk berkonsultasi langsung dengan para ahli pertanian. Metode pengembangan yang digunakan adalah pendekatan agile, dan pengujian aplikasi dilakukan melalui studi kasus pada petani di wilayah Riau. Hasil pengujian menunjukkan bahwa penggunaan aplikasi ini berhasil meningkatkan produktivitas pertanian hingga 25%, serta mengurangi penggunaan pupuk berlebih sebanyak 15%. Dengan demikian, aplikasi ini tidak hanya memberikan kemudahan dalam konsultasi tetapi juga berkontribusi pada efisiensi penggunaan sumber daya dan peningkatan hasil pertanian secara signifikan.
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 (IJCS)
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.
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 : Pusat Riset dan Inovasi Nasional

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.
PROTOTIPE INTEGRASI DATA MORBIDITAS PASIEN PUSKESMAS KEDALAM DATA WAREHOUSE DI DINAS KESEHATAN KABUTEN BANTUL Totok Suprawoto; Enny Itje Sela; Syamsu Windarti
Jurnal TAM (Technology Acceptance Model) Vol 7 (2016): Jurnal TAM (Technology Acceptance Model)
Publisher : LPPM STMIK Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v7i0.65

Abstract

Bantul District Health Office (DHO Bantul) is one of the agencies that are currently having problems to obtain health information that is accurate and current. The report should be made regularly by the health center then recapitulated in Bantul Health Office is a statement of outpatient morbidity such as Integrated Disease Surveillance (STP) report, a report based on the type of disease and others. The increasing number and complexity of morbidity data in Bantul Health Office environment, as well as the importance of planning and decision making, it is necessary to analyze and design data further using the data warehouse. From the analysis and design of data warehouse based on the fact constellation schema that includes dimensions: time, patient, age, disease and health centers, can then be further analyzed for purposes of making decisions using data mining. Furthermore, it can also be used to analyze patient data from multiple dimensions (time, patient, age, disease and health centers), and to analyze the growing number of patients from each period of benefit to the management of Bantul Health Office.
Analisis Perbandingan Fuzzy Tsukamoto dan Sugeno dalam Menentukan Jumlah Produksi Kain Tenun Menggunakan Base Rule Decision Tree Tundo, Tundo; Akbar, Riolandi; Sela, Enny Itje
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 1: Februari 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020701751

Abstract

Penelitian ini menerangkan tentang analisis perbandingan fuzzy Tsukamoto dan Sugeno dalam menentukan jumlah produksi kain tenun dengan menggunakan base rule decision tree. Dari hasil analisis penelitian ini, maka ditemukan beberapa perbedaan yang sangat signifikan: (1) Metode fuzzy Tsukamoto dari hasil yang diperoleh lebih mendekati dari data sesungguhnya, dibandingkan dengan fuzzy Sugeno, (2) Selisih yang diperoleh dengan menggunakan fuzzy Tsukamoto dengan data produksi sesungguhnya selalu konsisten yaitu hasil fuzzy Tsukamoto selalu lebih besar, sedangkan untuk fuzzy Sugeno tidak konsisten, (3) Hasil selisih untuk fuzzy Tsukamoto relatif mendekati dari data produksi sesungguhnya, sedangkan untuk fuzzy Sugeno relatif jauh selisih yang dihasilkan. Sehingga dapat disimpulkan bahwa metode yang paling mendekati nilai kebenaran adalah produksi yang mengunakan metode Tsukamoto dengan keakuratan yang diperoleh menggunakan base rule decision tree sebesar 83.3333 %.AbstractThis study describes the comparative analysis of fuzzy Tsukamoto and Sugeno determining the amount of woven fabric production using a decision tree base rule. From the results the analysis of this study, we found several very significant differences: (1) The fuzzy Tsukamoto method of the results obtained is closer to the actual, compared to fuzzy Sugeno, (2) The difference obtained by using fuzzy Tsukamoto with actual production data is always consistent is that Tsukamoto fuzzy results are always greater, while for Sugeno's fuzzy inconsistency, (3) The difference results for fuzzy Tsukamoto are relatively close to the actual production data, whereas Sugeno fuzzy is relatively far from the difference produced. So it can be concluded that the method closest to the truth value is production using the Tsukamoto method with the accuracy obtained using the base rule decision tree of 83.3333%.
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.
Co-Authors Adidarma, Muhamad Bahru Agus Ardiyanto Agus Ardiyanto Agus Harjoko Agus Harjoko AKBAR MAULANA Akbar, Riolandi Algadrie, Saniah Evatri Alhafiz, Amirrul Dafa 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 Ganda Sinarna Hendrawan, Ari Idham Kholed Rachmawan Julian Ega Prabowo Luther Alexander Latumakulita Luther Latumakulita M Ihsan Munakhir Mudjosemedi Nathaniela Aptanta Parama Noourul Islam Noviania, Reski Nugroho, Faishal Tirto Nur Wening, Nur Olwin Kirab Novaldy Prawirdani, Adil Rachmawan, Idham Kholed Retantyo Wardoyo Rianto Rianto Risky Devandra Hartana Saucha Diwandari Senoaji, Muhammad Senoaji Wibowo Sri Hartati Steven Ray Sentinuwo Syahrul Gunawan Ramdhani Syamsu Windarti Totok Suprawoto Triwibowo, Muhammad Ilham Tundo, Tundo Utami, Wahyu Sri