Article Per Year (5 Year)
p-Index From 2021 - 2026
14.689
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Proceedings of KNASTIK Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Informatika SPEKTRUM INDUSTRI Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknik Elektro Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Jurnal Pseudocode Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Jurnas Nasional Teknologi dan Sistem Informasi JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal Teknologi Elektro INFORMAL: Informatics Journal Proceeding SENDI_U Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Bulletin of Electrical Engineering and Informatics JOIN (Jurnal Online Informatika) Edu Komputika Journal Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Informatika Jurnal Khatulistiwa Informatika Journal of Information Technology and Computer Science (JOINTECS) Jurnal Ilmiah FIFO INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CogITo Smart Journal IT JOURNAL RESEARCH AND DEVELOPMENT InComTech: Jurnal Telekomunikasi dan Komputer Insect (Informatics and Security) : Jurnal Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL REKAYASA TEKNOLOGI INFORMASI PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Applied Information System and Management ILKOM Jurnal Ilmiah Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Sistem Informasi dan Aplikasi CYBERNETICS Digital Zone: Jurnal Teknologi Informasi dan Komunikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) JUMANJI (Jurnal Masyarakat Informatika Unjani) JURTEKSI RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Informatika : Jurnal Informatika, Manajemen dan Komputer Jurnal Ilmiah Mandala Education (JIME) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Systemic: Information System and Informatics Journal EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JISKa (Jurnal Informatika Sunan Kalijaga) Buletin Ilmiah Sarjana Teknik Elektro Mobile and Forensics Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Journal of Robotics and Control (JRC) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Cyber Security dan Forensik Digital (CSFD) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) International Journal of Advances in Data and Information Systems International Journal of Marine Engineering Innovation and Research Edunesia : jurnal Ilmiah Pendidikan Journal of Innovation Information Technology and Application (JINITA) Tematik : Jurnal Teknologi Informasi Komunikasi Infotech: Journal of Technology Information Jurnal Teknologi Informatika dan Komputer Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Humanism : Jurnal Pengabdian Masyarakat International Journal of Robotics and Control Systems J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Algoritma Techno Jurnal Pengabdian Informatika (JUPITA) Jurnal INFOTEL Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Jurnal Accounting Information System (AIMS) Scientific Journal of Informatics Control Systems and Optimization Letters Signal and Image Processing Letters Scientific Journal of Engineering Research SEMINAR TEKNOLOGI MAJALENGKA (STIMA) Edumaspul: Jurnal Pendidikan Methods in Science and Technology Studies JOCHAC
Abdul Fadlil
Ahmad Dahlan University
Religion 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 Dentistry 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 Neuroscience Physics Public Health Social Sciences Transportation Other

Published : 216 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 216 Documents
Search

 16   17   18   19   20 
Evaluating The Effectiveness of Augmentation and Classifier Algorithms for Fraud Detection: Comparing CGAN and SMOTE with Random Forest and XGBoost Sarmini, Sarmini; Sunardi, Sunardi; Fadlil, Abdul
Applied Information System and Management (AISM) Vol. 8 No. 2 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i2.46308

Abstract

Fraud detection in imbalanced datasets, where fraudulent transactions represent a small fraction of total data, presents a major challenge for machine learning models. Traditional classifiers often perform poorly in such scenarios due to their bias toward the majority class. This study investigates the effectiveness of two data augmentation techniques, Synthetic Minority Over-sampling Technique (SMOTE) and Conditional Generative Adversarial Networks (CGAN) in improving fraud detection performance. Both methods are applied to balance the dataset, and their impact is evaluated using two classifiers: Random Forest (RF) and XGBoost. The models are tested across three versions of the dataset: the original imbalanced data, the SMOTE-augmented data, and the CGAN-augmented data. Evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC. Results indicate that both augmentation techniques enhance the models' ability to detect fraudulent transactions compared to the original dataset. Notably, CGAN outperforms SMOTE in terms of recall and F1 score, suggesting its ability to generate more diverse and realistic synthetic samples. While SMOTE creates new samples through interpolation, CGAN uses an adversarial process involving a generator and a discriminator, resulting in more complex data representations. The study also finds that XGBoost combined with CGAN yields the highest performance, effectively capturing intricate fraud patterns. In contrast, SMOTE, though beneficial, shows limited capacity in improving recall. This research highlights the importance of advanced augmentation techniques like CGAN in addressing class imbalance and improving fraud detection systems. It also opens pathways for future exploration of deep learning-based augmentation and classification methods in fraud detection.
Comparison of Machine Learning Algorithms for Stunting Classification Yunus, Muhajir; Biddinika, Muhammad Kunta; Fadlil, Abdul
Scientific Journal of Engineering Research Vol. 1 No. 2 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i2.2025.9

Abstract

Indonesia is one of the countries with medium stunting data over the past decade, around 21.6%. Stunting prevention is a national program in Indonesia, and stunting reduction in children is the first of the six goals in the Global Nutrition Target for 2025. Based on SSGI data in 2022, the prevalence of stunting in Gorontalo Province is 23.8% and is in the high category. Stunting prevention is an early effort to improve the ability and quality of human resources. This study compared two Machine Learning algorithms for stunting classification in children, namely the Naive Bayes method and Decision Tree C4.5 using Python by dividing the training and testing data a total ratio of 80:20. The performance of each algorithm was evaluated using a dataset of child health information based on z-score calculation data with a total of 224 records, consisting of 4 attributes and 1 label, namely gender, age, weight, height and nutritional status. The results of the research that have been conducted show that the Decision Tree C4.5 algorithm achieves the highest accuracy in the classification of stunting events with an accuracy of 87% while for the Naïve Bayes algorithm produces a low accuracy of 71% so that for this study the Decision tree C4.5 algorithm is the best algorithm for the classification of stunting events. These findings suggest this algorithm can be a valuable tool for classifying children's stunting.
Air Quality Index Classification: Feature Selection for Improved Accuracy with Multinomial Logistic Regression Irjayana, Rizky Caesar; Fadlil, Abdul; Umar, Rusydi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5155

Abstract

Air pollution is a major public health concern, creating the need for accurate and interpretable Air Quality Index (AQI) classification models. This study aims to classify AQI into three categories—Good, Moderate, and Unhealthy—using Multinomial Logistic Regression (MLR) with feature selection. The dataset, obtained from public monitoring stations in Jakarta between 2021 and 2024, initially contained 4,620 daily records. After cleaning and outlier removal, 3,586 valid samples remained, from which 900 balanced records (300 per class) were selected for modeling. Key features included PM₁₀, PM₂.₅, SO₂, CO, O₃, and NO₂, which were standardized using Max Normalization to ensure uniform feature scaling. The classification process applied k-fold cross-validation (k = 2–5), and performance was assessed using accuracy and Macro F1-score. Results show that including PM₂.₅ improves performance by about 10%, with the best outcome at k = 5 (accuracy = 91.67%, Macro F1 = 91.45%). These findings confirm PM₂.₅ as a decisive feature for AQI prediction and demonstrate that MLR provides a lightweight, transparent, and computationally efficient solution. Beyond environmental health, the contribution of this work lies in advancing data-driven decision support systems in Informatics, particularly for real-time monitoring and policy applications.
PELATIHAN DATABASE ADMINISTRATOR SISWA SMK INFORMATIKA WONOSOBO Maftukhah, Ainin; Subandi, Rio; Umar, Rusydi; Fadlil, Abdul
JURNAL PENGABDIAN KEPADA MASYARAKAT Vol. 29 No. 4 (2023): OKTOBER-DESEMBER
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jpkm.v29i4.49159

Abstract

Pentingnya pengelolaan database yang efektif dalam dunia digital yang terus berkembang. Pentingnya pelatihan database untuk siswa dalam mengelola dan mengimplementasikan database menggunakan perintah SQL. Kegiatan pemberdayaan umat dilakukan dengan urutan langkah-langkah sebagai berikut, pertama persiapan melakukan studi literatur dan membuat database yang mudah dipahami oleh siswa. Kedua menyiapkan alat dan bahan pelatihan pembuatan database pembelajaran untuk mengelola data siswa. Ketiga mengidentifikasi dan menyiapkan materi, pretest, dan postest yang akan diberikan kepada peserta saat kegitan. Hasil kegiatan pemberdayaan umat yang dilaksanakan pada hari Senin, 12 Juni 2023 secara offline diikuti 20 siswa-siswi dari kelas X hingga XI SMK Informatika Wonosobo.Kegiatan pemberdayaan umat yang diselenggarakan menghasilkan pretest dan postest, terdapat perubahan pemahaman dan keterampilan peserta pelatihan administrator database. Hal ini ditunjukkan dengan nilai prestes 46,8%, sedangkan postest 48,5%.
Pengenalan Pola Depresi Berbasis Suara Menggunakan Ekstraksi Fitur Mel-Frequency Cepstral Coefficients Saputro, Wahju Tjahjo; Fadlil, Abdul; Murinto, Murinto
Jurnal PROCESSOR Vol 20 No 2 (2025): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2025.20.2.2513

Abstract

The identification of depression patterns from human voices is important because depression can interfere with activities, reduce interest in learning, and hinder socialisation. Depression is a significant problem today because there has been a global increase in the number of people suffering from it. The factors contributing to depression are numerous and complex, and can affect all groups, from children to the elderly. The purpose of this study was to identify depression patterns based on voice feature extraction. The feature extraction method used is Mel-Frequency Cepstral Coefficients (MFCC). The MFCC method is capable of extracting features that closely resemble the human auditory system. The dataset used is the EATD-Corpus, which contains 162 recordings of students from Tongji University in China. The results of the study show that depression and healthy patterns can be distinguished using MFCC parameters, namely 25 measurements per frame, 10 frame intervals, an alpha value of 0.97 as the pre-emphasis coefficient, a maximum of 40 Mel filterbank coefficients, and 12 cepstral coefficients. Classification thresholds can be obtained for two classes: healthy with thresholds < 53.00 and depressed ≥ 53.00 using the Self-Rating Depression Scale.
Face Recognition Using Machine Learning Algorithm Based on Raspberry Pi 4b Sunardi, Sunardi; Fadlil, Abdul; Prayogi, Denis
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (502.574 KB) | DOI: 10.29099/ijair.v7i1.321

Abstract

Machine learning is one of artificial intelligence that is used to solve various problems, one of which is classification. Classification can separate a set of objects based on certain characteristics. This study discusses the classification of objects in the form of facial images with the aim of the system being able to recognize a person's face to access a room for security reasons. The application of machine learning using the support vector machine algorithm with the support vector classifier technique is implemented on a raspberry pi-based security device.  The results of training using this algorithm produce a model with 99% accuracy in 0.10 seconds based on testing data of 525 face images. The model evaluation got 99% precision, 99% recall, and 99% f1-score. Testing the model made from the training process using the raspberry pi model 4b is can recognize facial images in real-time.  If the security device detects someone at the door and then recognizes the face image then room access will be granted and an alarm is activated indicating the door is open.
Analisis Metode AHP dan Promethee pada Sistem Pendukung Keputusan Penilaian Kompetensi Soft Skills Karyawan yuminah, Yuminah; Umar, Rusydi; Fadlil, Abdul
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

Abstract

Perusahaan sangat membutuhkan karyawan yang mempunyai kompetensi (soft skills) sikap dan perilaku yang baik untuk menghadapi orang lain dalam menyelesaikan pekerjaan, contohnya komunikasi, kejujuran, kerjasama dan interpersonal. Untuk melakukan penilaian kompetensi soft skills membutuhkan berbagai kriteria yang sangat beragam. Kriteria-kriteria yang terkait untuk menilai sikap dan perilaku sangat banyak, sehingga untuk melakukan penilaian kompetensi soft skills ini dengan hasil yang tepat dan cepat perusahaan mengalami kesulitan. Kondisi tersebut menunjukan bahwa perusahaan  membutuhkan  sebuah sistem yang dapat digunakan untuk penilaian kompetensi soft skills karyawan menggunakan alat bantu berupa komputer. Ada beberapa metode yang dapat digunakan untuk pengambilan keputusan dengan berbagai kriteria di antaranya AHP dan Promethee.  Maka fokus dalam penelitian ini adalah menggunakan metode AHP untuk menentukan pembobotan dan Promethee untuk pemeringkatan penilaian soft skills karyawan. Hasil pembobotan yang diperoleh untuk kriteria komunikasi 41%, kejujuran 38%, kerjasama 14% dan interpersonal 7%. Dengan rasio indeks konsistensi 6%. Dari jawaban responden diperoleh 58 % karyawan mempunyai kompetensi soft skills baik dan 42 % karyawan  kurang baik.AbstractCompanies that really need employees who need competencies (soft skills) Good attitudes and relationships to complete other people's work, for example communication, honesty, cooperation and interpersonal. To evaluate soft skills competencies requires a variety of criteria that are very diverse. The related criteria to assess attitudes and behavior are very many, so to evaluate this soft skills competency with the right and quick results the company has difficulties. This condition shows that companies need a system that can be used to assess the competency of employees' soft skills using computer-assisted tools. There are several methods that can be used for decision making with various criteria including AHP and Promethee. So the focus in this study is to use the AHP method to determine weighting and Promethee to rank the assessment of employee soft skills. The weighting results obtained for communication criteria were 41%, honesty 38%, cooperation 14% and interpersonal 7%. With a consistency index ratio of 6%. From the respondents' answers obtained 58% of employees have good soft skills competency and 42% of employees are not good.
Aplikasi Sistem Temu Kembali Angket Mahasiswa Menggunakan Metode Generalized Vector Space Model Suprianto, Suprianto; Fadlil, Abdul; Sunardi, Sunardi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 1: Februari 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2593.915 KB) | DOI: 10.25126/jtiik.2019611184

Abstract

Banyak hal yang dapat dilakukan untuk memajukan sebuah perguruan tinggi, salah satunya adalah dengan melakukan evaluasi terhadap angket Mahasiswa pada setiap semester. Salah satu perguruan tinggi yang ada di Kota Tarakan Kalimantan Utara adalah STMIK PPKIA Tarakanita Rahmawati. Banyaknya data yang terdapat pada angket mahasiswa PPKIA membuat pengguna kesulitan menemukan informasi yang sesuai dengan kata kunci yang diberikan. Angket mahasiswa berisi penilaian mahasiswa terhadap pengajaran dosen, pelayanan adminitrasi dan fasilitas kampus yang dibuat dalam bentuk form yaitu memilih grade nilai dari sangat tidak baik sampai dengan sangat baik. Selain itu juga terdapat penilaian dalam bentuk esay yaitu berupa saran dan komentar. Pengisian angket dilakukan pada akhir semester berjalan. Penelitian ini bertujuan untuk menemukan informasi data angket yang relevan terhadap kata kunci. Aplikasi dibangun berbasis web dengan bahasa pemrograman PHP. Aplikasi yang dibuat hanya menggunakan basisdata masih mempunyai kekurangan yaitu tidak dapat mengurutkan dokumen sesuai dengan kata kunci, dikarenakan pengurutan dokumen hanya berdasarkan urutan dokumen pada basisdata saja. Dengan memanfaatkan teknik information retrieval (IR) yang diterapkan pada aplikasi, pengguna akan sangat terbantu dalam menemukan informasi yang dibutuhkan. Aplikasi yang dibuat dapat menampilkan dan mengurutkan dokumen yang paling mirip dengan kata kunci. Aplikasi dibangun dengan metode Generalized Vector Space Model (GVSM) sebagai dasar untuk menyelesaikan permasalahan yang ada. Metode GVSM adalah IR atau biasa disebut sistem temu kembali untuk mencocokkan term atau kata dari kata kunci yang digunakan. Dari hasil uji coba terhadap 5 kata kunci diperoleh nilai precision sebesar 72% dan recall sebesar 100% dengan waktu proses selama 34.4 detik AbstractMany things can be done to advance a university, one of which is by evaluating the Student questionnaire every period. One of the universities in Tarakan City, North Kalimantan is STMIK PPKIA Tarakanita Rahmawati. The large amount of data contained in PPKIA student questionnaires makes it difficult for users to find information that matches the given keywords. Student questionnaires contain student assessments of lecturer teaching, administrative services and campus facilities that are made in the form of selecting grades from very bad to very good. In addition there are also assessments in the form of essays, in the form of suggestions and comments. The questionnaire will be filled in at the end of the period. This study aims to find questionnaire data information that is relevant to the keyword. The application is built web-based with the PHP programming language. Applications that are made using only databases still have a disadvantage of not being able to sort documents according to keywords, because sorting documents is only based on the order of documents on the database only. By utilizing information retrieval (IR) techniques that are applied to the application, users will be very helpful in finding the information needed. The application created can display and sort documents that are most similar to keywords. Applications are built with the Generalized Vector Space Model (GVSM) method as a basis for solving existing problems. The GVSM method is IR or commonly called a retrieval system to match the terms or words of the keywords used. From the results of trials on 5 keywords, the precision value of 72% and recall of 100% were obtained with a processing time of 34.4 seconds.
Analisis Perbandingan Metode Regresi Linier Dan Importance Performance Analysis (IPA) Terhadap Kepuasan Pengguna Pada Layanan E-Government Menggunakan Metode WebQual Modifikasi Septa, Frandika; Yudhana, Anton; Fadlil, Abdul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 5: Oktober 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020752294

Abstract

Layanan excellent merupakan layanan yang mampu memberikan rasa kepuasan bagi penggunanya, sehingga menimbulkan loyalitas terhadap layanan yang digunakan. Layanan E-Government menjadi penting untuk dilakukan penilaian kualitas layanannya terhadap kepuasan pengguna, karena salah satu tujuan dikembangkannya E-Government adalah memberikan pelayanan yang maksimal kepada masyarakat. Penelitian ini bertujuan untuk menganalisis tingkat kualitas layanan E-Government dalam kasus ini website SIMSARPRAS berdasarkan persepsi pengguna dari website SIMSARPRAS, yaitu madrasah dan operator kementerian agama. Jumlah responden adalah 500 orang yang dikumpulkan dari hasil penyebarah kuesioner secara online menggunakan Google Formulir. Metode dalam penelitian ini menggunakan metode WebQual modifikasi sebagai indikator dalam penyusunan kuesioner secara online, dan metode analisa menggunakan regresi linier berganda dan importance performance analysis (IPA). Hasil kuesioner diolah dan dilakukan pengujian instrumen dengan uji validitas dan uji reliabilitas untuk menunjukkan bahwa kuesioner layak dijadikan sebagai bahan penelitian lebih lanjut untuk dilakukan analisis data. Hasil analisis data diklasifikasikan berdasarkan persentase kepuasan pengguna terhadap layanan webiste SIMSARPRAS dengan tiga klasifikasi, yaitu baik, sedang dan buruk. Hasil analisis data menggunakan regresi linier berganda menunjukkan bahwa website SIMSARPRAS berkualitas sedang, sedangkan dengan IPA website berkualitas baik. Hasil analisis data menggunakan regresi linier berganda diketahui bahwa variabel bebas mampu mempengaruhi kepuasan pengguna sebesar  67,6% dan 32,4% dipengaruhi oleh variabel lainnya, sedangkan hasil dari IPA tingkat kesesuaian sebesar 96,22% dan tingkat kesenjangan antara kinerja dan harapan dari layanan E-Government sebesar –0,12. Hasil dari penelitian ini memberikan kontribusi kepada kementerian agama khususnya sebagai pemilik website SIMSARPRAS untuk dijadikan sebagai bahan referensi dan evaluasi layanan SIMSARPRAS kedepannya. AbstractExcellent service is a service that provides a sense of satisfaction for its users, thereby giving rise to loyalty to the services used. E-Government services are important to do. E-Government services to user satisfaction, because one of the goals that E-Government is developing to provide maximum service to the community. This study analyzes the level of quality of E-Government services in this case the SIMSARPRAS website based on user perceptions from the SIMSARPRAS website, namely madrasah and operators of the ministry of religion. The number of respondents was 500 people collected from the results of a questionnaire search using Google Forms. The method in this study uses the WebQual modification method as an indicator in testing online questionnaires, and the analysis method uses multiple linear regression and performance analysis of interest (IPA). The results of the questionnaire were processed and tested by an instrument with a validity test and a reliability test to prove the questionnaire was worthy of being used as further research material for data analysis. The results of data analysis are based on the percentage of user satisfaction with the SIMSARPRAS website service with three classifications, namely good, moderate and bad. The results of data analysis using multiple linear regression showed a medium-quality SIMSARPRAS site, whereas with a good quality website IPA. The results of data analysis using multiple linear regression owned by independent variables can increase user satisfaction by 67.6% and 32.4% required by other variables, while the results of the IPA level of suitability are 96.22% and the level is in accordance with the results and expectations of the service E-Government is -0.12. The results of this study contribute to the special ministry of religion as the owner of the SIMSARPRAS website for use as a reference material and SIMSARPRAS service solutions going forward.
Identifikasi Emosi Manusia Berdasarkan Ucapan Menggunakan Metode Ekstraksi Ciri LPC dan Metode Euclidean Distance Helmiyah, Siti; Riadi, Imam; Umar, Rusydi; Hanif, Abdullah; Yudhana, Anton; Fadlil, Abdul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722693

Abstract

Ucapan merupakan sinyal yang memiliki kompleksitas tinggi terdiri dari berbagai informasi. Informasi yang dapat ditangkap dari ucapan dapat berupa pesan terhadap lawan bicara, pembicara, bahasa, bahkan emosi pembicara itu sendiri tanpa disadari oleh si pembicara. Speech Processing adalah cabang dari pemrosesan sinyal digital yang bertujuan untuk terwujudnya interaksi yang natural antar manusia dan mesin. Karakteristik emosional adalah fitur yang terdapat dalam ucapan yang membawa ciri-ciri dari emosi pembicara. Linear Predictive Coding (LPC) adalah sebuah metode untuk mengekstraksi ciri dalam pemrosesan sinyal. Penelitian ini, menggunakan LPC sebagai ekstraksi ciri dan Metode Euclidean Distance untuk identifikasi emosi berdasarkan ciri yang didapatkan dari LPC.  Penelitian ini menggunakan data emosi marah, sedih, bahagia, netral dan bosan. Data yang digunakan diambil dari Berlin Emo DB, dengan menggunakan tiga kalimat berbeda dan aktor yang berbeda juga. Penelitian ini menghasilkan akurasi pada emosi sedih 58,33%, emosi netral 50%, emosi marah 41,67%, emosi bahagia 8,33% dan untuk emosi bosan tidak dapat dikenali. Penggunaan Metode LPC sebagai ekstraksi ciri memberikan hasil yang kurang baik pada penelitian ini karena akurasi rata-rata hanya sebesar 31,67% untuk identifikasi semua emosi. Data suara yang digunakan dengan kalimat, aktor, umur dan aksen yang berbeda dapat mempengaruhi dalam pengenalan emosi, maka dari itu ekstraksi ciri dalam pengenalan pola ucapan emosi manusia sangat penting. Hasil akurasi pada penelitian ini masih sangat kecil dan dapat ditingkatkan dengan menggunakan ekstraksi ciri yang lain seperti prosidis, spektral, dan kualitas suara, penggunaan parameter max, min, mean, median, kurtosis dan skewenes. Selain itu penggunaan metode klasifikasi juga dapat mempengaruhi hasil pengenalan emosi. AbstractSpeech is a signal that has a high complexity consisting of various information. Information that can be captured from speech can be in the form of messages to interlocutor, the speaker, the language, even the speaker's emotions themselves without the speaker realizing it. Speech Processing is a branch of digital signal processing aimed at the realization of natural interactions between humans and machines. Emotional characteristics are features contained in the speech that carry the characteristics of the speaker's emotions. Linear Predictive Coding (LPC) is a method for extracting features in signal processing. This research uses LPC as a feature extraction and Euclidean Distance Method to identify emotions based on features obtained from LPC. This study uses data on emotions of anger, sadness, happiness, neutrality, and boredom. The data used was taken from Berlin Emo DB, using three different sentences and different actors. This research resulted in inaccuracy in sad emotions 58.33%, neutral emotions 50%, angry emotions 41.67%, happy emotions 8.33% and bored emotions could not be recognized. The use of the LPC method as feature extraction gave unfavorable results in this study because the average accuracy was only 31.67% for the identification of all emotions. Voice data used with different sentences, actors, ages, and accents can influence the recognition of emotions, therefore the extraction of features in the recognition of speech patterns of human emotions is very important. Accuracy results in this study are still very small and can be improved by using other feature extractions such as provides, spectral, and sound quality, using parameters max, min, mean, median, kurtosis, and skewness. Besides the use of classification methods can also affect the results of emotional recognition. 
Co-Authors Aang Anwarudin Abdul Azis Achmad Nugrahantoro Aditiya Dwi Candra Ahmad Naufal, Ahmad Ahmat Taufik Aji Pamungkas Akrom, Akrom Alfiansyah Imanda Putra Alfiansyah Imanda Putra Alfian Amiruddin, Nanda Fahmi Andrianto, Fiki Anggit Pamungkas Annisa, Putri Anton Yudhana Anwar Siswanto ANWAR, FAHMI ardi, Ardi Pujiyanta Arief Setyo Nugroho Arief Setyo Nugroho Arif Budi Setianto Arif Budiman Arif Budiman Arif Wirawan Muhammad Aris Rakhmadi Asep Ririh Riswaya Asno Azzawagama Firdaus Atmojo, Dimas Murtia Aulia, Aulia Az-Zahra, Rifqi Rahmatika Aznar Abdillah, Muhamad Bagus Primantoro Bashor Fauzan Muthohirin Basir, Azhar Budiman, Dheni Apriantsani Candra, Aditiya Dwi Darajat, Muhammad Nashiruddin Davito Rasendriya Rizqullah Putra Dewi Soyusiawaty Dewi Soyusiawaty Dhimas Dwiki Sanjaya Dian Permata Sari Dianda Rifaldi Dikky Praseptian M Dimas Murtia Atmojo Doddy Teguh Yuwono Dwi Susanto Dwi Susanto Edy Fathurrozaq Egi Dio Bagus Sudewo Eko Budi Cahyono Eko Prianto Eko Prianto Elvina, Ade Ermin Al Munawar Ermin Ermin Esthi Dyah Rikhiana Fahmi Anwar Fahmi Auliya Tsani Fahmi Auliya Tsani Fahmi Fachri Fanani, Galih Faqihuddin Al-anshori Faqihuddin Al-Anshori, Faqihuddin Fathurrahman, Haris Imam Karim Fauzi Hermawan Fiki Andrianto Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Yasin Fitri Muwardi Furizal Gusrin, Muhaimin Gustina, Sapriani Hafizh, Muhammad Nasir Haksono, Muhammad Rizky Hanif, Abdullah Hanif, Kharis Hudaiby Harman, Rika Helmiyah, Siti Hendril Satrian Purnama Herdiyanto, Erik Herman Herman Herman Yuliansyah, Herman Herman, - Ibnu Rifajar Ibrahim Mohd Alsofyani Ibrahim, Rohmat Ihyak Ulumuddin Ikhsan hidayat Ilhamsyah Muhammad Nurdin Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Irjayana, Rizky Caesar Irwansyah Irwansyah Izzan Julda D.E Purwadi Putra januari audrey Jayawarsa, A.A. Ketut Jogo Samodro, Maulana Muhamammad Joko Supriyanto Joko Supriyanto Kamilah, Farhah Kartika Firdausy Khoirunnisa, Itsnaini Irvina Kusuma, Nur Makkie Perdana Laura Sari Lestari, Yuniarti Lin, Yu-Hao Luh Putu Ratna Sundari M. Nasir Hafizh Maftukhah, Ainin Maulana Muhammad Jogo Samudro Mini, Ros Mohd Hatta Jopri Muammar Mudinillah, Adam Mufaddal Al Baqir Muh. Fadli Hasa Muhaimin Gusrin Muhajir Yunus Muhamad Daffa Al Fitra Muhamad Rosidin Muhammad Faqih Dzulqarnain, Muhammad Faqih Muhammad Johan Wahyudi Muhammad Kunta Biddinika Muhammad Ma’ruf Muhammad Nasir Hafizh Muhammad Nur Faiz Muhammad Nurdin, Ilhamsyah Muhammad Rizki Setyawan Mukti, Sindhu Hari Muntiari, Novita Ranti Murinto Murinto - Murinto Murinto Murni Murni Musliman, Anwar Siswanto Mustofa Mustofa Muthorihin, Bashor Fauzan Mutiara Titani Muwardi, Fitri Nasution, Dewi Sahara Nasution, Musri Iskandar Nilam Tri Astuti Nurwijayanti Pahlevi, Ryan Fitrian Ponco Sukaswanto Poni Wijayanti Prabowo Soetadji Prabowo, Basit Adhi Prayogi, Denis Priambodo, Bambang Putra, Fajar R. B Putri Annisa Putri Annisa Putri Purnamasari Putri Silmina, Esi Ramadhani, Muhammad Ramdhani, Rezki Razak, Farhan Radhiansyah Rezki Rezki Rifqi Rahmatika Az-Zahra Rizky Andhika Surya Rochmadi, Tri Roni Anggara Putra Rusydi Umar Rusydi Umar S Sunardi S, Sunardi Saad, Saleh Khalifah Safiq Rosad Saifudin Saifudin Saifullah, Shoffan Saleh khalifa saad Santi Purwaningrum Sarmini Sarmini Septa, Frandika Setyaputri, Khairina Eka Setyaputri, Khairina Eka Setyaputri, Khairina Eka Shinta Nur Desmia Sari Siswahyudianto Siti Helmiyah Sri Winiarti Subandi, Rio Sukaswanto, Ponco Sukma Aji Sulis Triyanto Sunardi Sunardi Sunardi Sunardi, Sunardi Surya Yeki Surya Yeki Syamsiar, Syamsiar Syarifudin, Arma Tole Sutikno Tresna Yudha Prawira Tri Ferga Prasetyo Tristanti, Novi Tuswanto Tuswanto Virdiana Sriviana Fatmawaty Wahju Tjahjo Saputro Wahyusari, Retno Winoto, Sakti Wintolo, Hero Wulandari, Cisi Fitri Yana Mulyana Yana Mulyana Yasidah Nur Istiqomah Yeki, Surya Yohanni Syahra Yossi Octavina Yuantoro, Jody Yulianto, Dinan Yulianto, Muhammad Anas Yuminah yuminah yuminah, Yuminah Yuminah, Yuminah Yuwono Fitri Widodo Zein, Wahid Alfaridsi Achmad Zulhijayanto -