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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika International Journal of Advances in Intelligent Informatics E-Dimas: Jurnal Pengabdian kepada Masyarakat International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science JURNAL MEDIA INFORMATIKA BUDIDARMA International Journal of Computing and Informatics (IJCANDI) JITK (Jurnal Ilmu Pengetahuan dan Komputer) ILKOM Jurnal Ilmiah Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) EDUMATIC: Jurnal Pendidikan Informatika Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Indonesian Journal of Data and Science Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat Bulletin of Culinary Art and Hospitality Jurnal Inovasi Teknologi dan Edukasi Teknik NERO (Networking Engineering Research Operation) Jurnal Informatika: Jurnal Pengembangan IT Linier: Literatur Informatika dan Komputer
Purnawansyah Purnawansyah
Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia
Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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ANALISIS KINERJA ALGORITMA PEMBELAJARAN MESIN ENSEMBEL PADA DATASET MULTI KELAS CITRA JAFFE Azis, Huzain; Alisma, Alisma; Purnawansyah, Purnawansyah; Nirmala, Nirmala
NERO (Networking Engineering Research Operation) Vol 9, No 2 (2024): Nero - 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i2.27872

Abstract

This research aims to develop a facial expression recognition system based on the JAFFE dataset which includes seven classes of emotional expressions, namely happy, sad, angry, afraid, disgusted and neutral expressions. The first step taken is canny segmentation on each dataset to maintain essential information on each face. Next, extraction was carried out using the hu moments method to gain an in-depth understanding of the important characteristics of facial expressions. The next process involves ensemble voting using five classification methods, namely Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gaussian Process Classifier (GPC), and Decision Tree. The results of these five methods are then ensembel using voting techniques, and the final results are evaluated using performance metrics such as accuracy, precision, recall, and F-1 score. Evaluation is carried out by comparing the final results with the original data from the JAFFE dataset, by measuring accuracy , precision, recall, and F1 Score value to evaluate system performance. The results of this research show that the ensemble voting approach using a combination of classification methods is able to significantly improve facial expression recognition capabilities. The resulting accuracy, precision, recall, and F1 Score values provide a comprehensive picture of system performance.  This research contributes to the development of facial emotion recognition technology and can be applied in various contexts. Includes human-computer interaction as well as applications in the fields of artificial intelligence.Keywords: Performance Analysis, Ensemble, Jaffe Image, Classification, Multiclass
Pemilihan Alternatif Karir Mahasiswa Fakultas Ilmu Komputer Menggunakan Metode TOPSIS Arman, Eka; Mansyur, St. Hajrah; Purnawansyah, Purnawansyah
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 5, No 3 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v5i3.2242

Abstract

Terdapat fenomena dimana para sarjana yang baru lulus belum sepenuhnya mempertimbangkan kemampuan dan minatnya dalam memilih suatu pekerjaan. Mahasiswa di Indonesia terutama jurusan atau program studi Teknik Informatika yang belum menyiapkan karirnya. Maka dari itu dibutuhkan sistem pendukung keputusan yang mampu memberikan rekomendasi karir kepada mahasiswa terutama dalam bidang Teknik Informatika. Hasil alternatif rekomendasi pemilihan karir terbaik menggunakan metode TOPSIS. Dengan menggunakan metode tersebut penelitian ini menghasilkan sistem pendukung keputusan pemilihan alternatif karir mahasiswa berbasis web yang dibuat sesuai dengan kebutuhan perusahaan. Berdasarkan perhitungan metode TOPSIS menghasilkan alternatif karir mahasiswa sesuai dengan kompetensi keilmuan, yaitu Pemrograman aplikasi dan perangkat lunak dengan nilai 0,53, Spesialisasi jaringan dan infrastruktur dengan nilai 0,48, Ahli operasi dan sistem dengan nilai 0,30, Konsultan teknologi dan informasi dengan nilai 0,80, Pengembangan web designer UI/UX dengan nilai 0,48. Adapun hasil pengujian akurasi dari 50 data uji terdapat 48 data yang sesuai dan 2 data yang tidak sesuai, sehingga diperoleh nilai akurasi sebesar 96%. Web pemilihan alternatif karir mahasiswa telah melalui uji coba menggunakan metode blackbox testing serta penyebaran kuesioner dengan 33 responden untuk aspek antarmuka, aspek kinerja, aspek database serta aspek inisialisasi. Maka dari itu diperoleh hasil dari keseluruhan sebesar 81,2% yang termasuk dalam kriteria baik.
Congestion Predictive Modelling on Network Dataset Using Ensemble Deep Learning Purnawansyah, Purnawansyah; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna; Haviluddin, Haviluddin; Raja, Roesman Ridwan; Darwis, Herdianti; Nafalski, Andrew
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.333

Abstract

Network congestion arises from factors like bandwidth misallocation and increased node density leading to issues such as reduced packet delivery ratios and energy efficiency, increased packet loss and delay, and diminished Quality of Service and Quality of Experience. This study highlights the potential of deep learning and ensemble learning for network congestion analysis, which has been less explored compared to packet-loss based, delay-based, hybrid-based, and machine learning approaches, offering opportunities for advancement through parameter tuning, data labeling, architecture simulation, and activation function experiments, despite challenges posed by the scarcity of labeled data due to the high costs, time, computational resources, and human effort required for labeling. In this paper, we investigate network congestion prediction using deep learning and observe the results individually, as well as analyze ensemble learning outcomes using majority voting, from data that we recorded and clustered using K-Means. We leverage deep learning models including BPNN, CNN, LSTM, and hybrid LSTM-CNN architectures on 12 scenarios formed out of the combination of level datasets, normalization techniques, and number of recommended clusters and the results reveal that ensemble methods, particularly those integrating LSTM and CNN models (LSTM-CNN), consistently outperform individual deep learning models, demonstrating higher accuracy and stability across diverse datasets. Besides that, it is preferably recommended to use the QoS level dataset and the combinations of 3 clusters due to the most consistent evaluation results across different configurations and normalization strategies. The ensemble learning evaluation results show consistently high performance across various metrics, with accuracy, Matthews Correlation Coefficient, and Cohen's Kappa values nearing 100%, indicates excellent predictive capability and agreement. Hamming Loss remains minimal highlighting the low misclassification rates. Notably, this study advances predictive modeling in network management, offering strategies to enhance network efficiency and reliability amidst escalating traffic demands for more sustainable network operations.
Rancang Bangun Sistem Manajemen Data Akreditasi berbasis Web Asis, Muhammad Arfah; Purnawansyah, Purnawansyah; Salim, Yulita
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 10 No 1 (2024): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v10i1.2989

Abstract

Akreditasi memerlukan pengelolaan dokumen yang efisien, namun di banyak universitas, pengelolaan dokumen akreditasi masih menghadapi kendala. Dokumen tersebar dalam berbagai format dan sulit diakses. Oleh karena itu, pengembangan sistem informasi manajemen data akreditasi menjadi penting. Tujuan penelitian ini untuk merancang dan membangun sistem manajemen data akreditasi berbasis web yang sesuai dengan kebutuhan Fakultas Ilmu Komputer di Universitas Muslim Indonesia (UMI). Penelitian ini menggunakan metode waterfall dalam pengembangan sistem dengan tahapan requirements, design, implementation, testing, dan maintenance. Sistem ini memungkinkan admin dan operator mengelola data akreditasi, dan asesor untuk mengakses dan mengevaluasi dokumen akreditasi. Hasil pengujian menunjukkan bahwa sistem dapat berjalan sesuai dengan yang diharapkan, dan semua fitur utama berfungsi dengan baik. Kesimpulannya, sistem ini membantu Fakultas Ilmu Komputer UMI dalam meningkatkan efisiensi dalam proses akreditasi, menghemat waktu dan sumber daya, serta mendukung pemeliharaan kualitas dan reputasi pendidikan tinggi di fakultas.
Ensemble semi-supervised learning in facial expression recognition Purnawansyah, Purnawansyah; Adnan, Adam; Darwis, Herdianti; Wibawa, Aji Prasetya; Widyaningtyas, Triyanna; Haviluddin, Haviluddin
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1880

Abstract

Facial Expression Recognition (FER) plays a crucial role in human-computer interaction, yet improving its accuracy remains a significant challenge. This study aims to enhance the robustness and effectiveness of FER systems by integrating multiple machine learning techniques within a semi-supervised learning framework. The primary objective is to develop a more effective ensemble model that combines Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector Classifier (SVC), and Random Forest classifiers, utilizing both labeled and unlabeled data. The research implements data augmentation and feature extraction techniques, utilizing advanced architectures such as VGG19, ResNet50, and InceptionV3 to improve the quality and representation of facial expression data. Evaluations were conducted across three dataset scenarios: original, feature-extracted, and augmented, using various label-to-unlabeled ratios. The results indicate that the ensemble model achieved a notable accuracy improvement of 87% on the augmented dataset compared to individual classifiers and other ensemble methods, demonstrating superior performance in handling occlusions and diverse data conditions. However, several limitations exist. The study’s reliance on the JAFFE dataset may restrict its generalizability, as it may not cover the full range of facial expressions encountered in real-world scenarios. Additionally, the effect of label-to-unlabeled ratios on the model's performance requires further exploration. Computational efficiency and training time were also not evaluated, which are critical considerations for practical implementation. For future research, it is recommended to employ cross-validation methods for more robust performance evaluation, explore additional data augmentation techniques, optimize ensemble configurations, and address the computational efficiency of the model to better advance FER technologies.
Analisis Tata Letak Koleksi Buku Di Perpustakaan Utsman Bin Affan Menggunakan Metode Association Rule Anugrah, Rezky; Purnawansyah, Purnawansyah; Astuti, Wistiani
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 5, No 4 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v5i4.2166

Abstract

Universitas Muslim Indonesia telah berhasil menempati posisi ke-92 pemeringkatan WRWU. Keberhasilan atas pencapaian UMI didukung dengan adanya berbagai sarana dan prasarana salah satunya fasilitas perpustakaan. Pada perpustakaan Utsman Bin Affan buku diletakkan berdasarkan kategori buku yang telah diberikan penomoran yang disebut Dewey Decimal Classification (DDC). Namun, dalam penempatan buku belum diatur dengan melihat tingkat keseringan pengunjung dalam meminjam buku tersebut. Selain itu, pengunjung kesulitan dalam mencari kembali keberadaan buku yang sering dipinjam. Metode association rule khususnya algoritma apriori dapat digunakan dalam penataan koleksi buku di perpustakaan Utsman Bin Affan untuk mengidentifikasi asosiasi antara berbagai judul buku dengan menemukan support dan confidence yang menghasilkan pola asosiasi. Dari data transaksi peminjaman buku sejak 21 Januari 2022 sampai 19 januari 2024 adalah sebanyak 50 ID Mahasiswa perpustakaan dengan total 128 transaksi menghasilkan pola transaksi peminjaman mahasiswa UMI yakni Karya Umum, Ilmu-Ilmu Sosial dengan nilai support 14% dengan confidence 43%. Buku Filsafat dan Psikologi, Agama dengan nilai support 22% dengan confidence 36%. Selanjutnya buku Filsafat dan Psikologi maka mahasiswa juga akan meminjam ilmu-Ilmu Sosial dengan nilai support 22% dengan confidence 45%. Berdasarkan hasil tersebut disarankan untuk melakukan evaluasi reguler terhadap tata letak perpustakaan berdasarkan data penyimpanan dan umpan balik pengguna, dan melakukan penyesuaian yang diperlukan untuk meningkatkan efektifitasnya.
Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Convolutional Neural Network dengan Ekstraksi Fourier Descriptor Basri, Haerunnisa; Purnawansyah, Purnawansyah; Darwis, Herdianti; Umar, Fitriyani
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 2 (2023): Desember 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i2.10350

Abstract

The number of herbal plants in Indonesia is 30,000, but only about 1,200 plants are used in medicine. The large number of herbal plants makes it difficult for people to distinguish one type of herbal plant from another. From these conditions, this research has conducted tests to compare the performance of the K-Nearest Neighbor (KNN) and Convolutional Neural Network (CNN) methods using Fourier Descriptor (FD) feature extraction on herbal plants, namely moringa (moringa oleifera) and katuk (sauropus androgynus). The amount of data used is 480 data using image conditions, namely dark and light images which are then divided into 20% testing data and 80% training data. Classification is done using the KNN method using 5 distance calculations (Euclidean, Chebyshev, Manhattan, Minkowski, and Hamming) and CNN with FD feature extraction. From the tests that have been carried out, it is found that the use of FD feature extraction for the KNN method produces the best performance on both light and dark image data. While the use of the CNN method, for dark image data, the best accuracy results are obtained with FD feature extraction and CNN. Meanwhile, for bright image data, the best performance accuracy results are obtained in the CNN method without going through feature extraction. Of these three methods, using FD and KNN feature extraction is more recommended because it produces 100% accuracy in moringa and katuk images with light and dark intensity.
A Comparative Study of Public Opinion on Indonesian Police: Examining Cases in the Aftermath of the Kanjuruhan Football Disaster Purnawansyah, Purnawansyah; Raja, Roesman Ridwan; Darwis, Herdianti
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.235

Abstract

This research explores public sentiment towards the Indonesian police using sentiment analysis and machine learning techniques. The study addresses the challenge of understanding public opinion based on social media comments related to significant police cases. The aim is to compare reported satisfaction levels with actual public sentiment. Utilizing the Indonesian RoBERTa base IndoLEM sentiment classifier, comments were analyzed and preprocessed. The classification was conducted using Random Forest (RF) and Complement Naive Bayes (CNB) models, incorporating unigram and bi-gram features. Oversampling techniques were applied to handle data imbalance. The best-performing model, Random Forest with bi-gram features, achieved high evaluation scores, including a precision of 0.91 and accuracy of 0.91. The findings reveal significant insights into public opinion, contributing to improved law enforcement strategies and public trust.
An Inflation Rate Prediction Based on Backpropagation Neural Network Algorithm Purnawansyah, Purnawansyah; Haviluddin, Haviluddin; Setyadi, Hario Jati; Wong, Kelvin; Alfred, Rayner
International Journal of Artificial Intelligence Research Vol 3, No 2 (2019): December 2019
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1806.11 KB) | DOI: 10.29099/ijair.v3i2.112

Abstract

This article aims to predict the inflation rate in Samarinda, East Kalimantan by implementing an intelligent algorithm, Backpropagation Neural Network (BPNN). The inflation rate data was obtained from the Provincial Statistics Bureau of Samarinda https://samarindakota.bps.go.id/ for the period January 2012 to January 2017. The method used to measure accuracy algorithm prediction was the mean square error (MSE). Based on the experiment results, the BPNN method with architectural parameters of 5-5-5-1; the learning function was trainlm; the activation functions were logsig and purelin; the learning rate was 0.1 and able to produce a good level of prediction error with an MSE value of 0.00000424. The results showed that the BPNN algorithm can be used as an alternative method in predicting inflation rates in order to support sustainable economic growth, so that it can improve the welfare of the people in Samarinda, East Kalimantan.
Literasi dan Pendampingan Pengelolaan Website Fakultas Ekonomi dan Bisnis Untuk Peningkatan Peringkat UMI di Webometrics Berdasarkan Aspek Penilaian Visibility Irawati, Irawati; Purnawansyah, Purnawansyah; Indra, Dolly
Ilmu Komputer untuk Masyarakat Vol 5, No 1 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkomas.v5i1.2292

Abstract

Webometrics adalah metodologi yang digunakan untuk memberikan pemeringkatan pada perguruan tinggi di seluruh dunia. Sebagai salah satu perguruan tinggi swasta terbesar dan terbaik di Indonesia Timur, UMI berupaya untuk meningkatkan mutu pendidikannya setiap tahun. Setiap kegiatan yang dilakukan oleh UMI akan tercatat website pemeringkatan universitas dunia yang disebut Webometrics. Agar dapat menunjang indikator-indikator pencapaian webometrics UMI, maka civitas akademika UMI perlu memahami tentang pentingnya pengetahuan tentang indikator yang dinilai oleh Cybermetrics Lab sebagai metode pemeringkatan perguruan tinggi. Kegiatan yang dilakukan berupa Sosialisasi dan workshop  Webometrix tentang Visibility Impact (50%), jumlah eksternal link unik yang terhubung dengan domain web milik perguruan tinggi. 
Co-Authors - Nurhikma A. Nurjulianty Abd. Rasyid Syamsuri Abdul Rachman Manga’ Achmad Fanany Onnilita Gaffar Achmad Fanany Onnilita Gaffar Adela Regita Azzahra Adnan, Adam Agung R Aji P. Wibawa Aji Prasetya Wibawa Alfitriana Riska Alfiyyah, Nurul Alisma, Alisma Andi Muhammad Adnan Rusdy Andri Rajsya Anisatul Humairah Anugrah, Rezky Arman, Eka Arrosied, Harun Arvina Yudithia Sompie Astuti, Wistiani Atussaliha, Nur Almar' Awang Harsa Kridalaksana Awangga, Narendra Backar, Sunarti Passura Basri, Haerunnisa Benny Leonard Enrico P Benny Leonard Enrico Panggabean Bustam, Faida Daeng Darwis, Herdianti Desi Anggreani Dewi Widyawati Dian Dolly Indra Dwiyanto, Felix Andika Enrico Panggabean, Benny Leonard Fahmi Fahmi Fajar AR, Muh. Aqil Faradibah, Amaliah Farniwati Fattah Fatimah Syarifuddin Fattah, Farniwati Fery Setyo Aji Firdaus, Muhammad Nur Firman Akbar Fitriyani Umar Harlinda L Harlinda Lahuddin Hartanto, Kotot Tri Hasihi, Cholisah Erman Hasnidar S. Hasrah Wahyuni Haviluddin Haviluddin Herawati Herawati Herdianti Darwis Herman Herman Huzain Azis Ifan Wahyudi Irawati Irawati Irawati Irawati Iriani Indah Saputri jabir, sitti rahmah Jumrayanti Arfah Kasmira Kasmira Kasmira, Kasmira La Saiman Lilis Hayati lilis nurhayati Listyan Nur Saida Lokapitasari Belluano, Poetri Lestari Lukman Syafie Lutfi Budiman Ilmuwan M. Imam Maulana M. Takdir Mahfuddin Mukmin Malani, Rheo Manga', Abdul Rachman Manga, Abdul Rachman Mansyur, St. Hajrah Mardiyyah Hasnawi Ming Foey Teng Ming Foey Teng, Ming Foey Muh Alim Abdi Muh. Fadhil Attariq Hasril Muh. Rifqi Zulkifli Muhammad Arfah Asis Muhammad Arfah Iswaniah Muhammad Hardiansyah Hairi Muhammad Ikhsan Supriyadi Muhammad Yushar Mattola Munaf, Adryan Dwiprawira Munawir Nasir Hamzah Nafalski, Andrew Nia Kurniati Nirmala Nirmala, Nirmala Nirwana, Nirwana Nugroho, Basuki Rahmat Nur Afra Dimitri Pratiwi Nur Almar' Atussaliha Nur Rahmah NURZAENAB NURZAENAB NURZAENAB, NURZAENAB Panggabean, Benny Leonard Enrico Purba, Muren Fiatra Denata Putri Regina Prayoga Putri, Inggrianti Pratiwi Rahma Puspitasari Rahmadani Rahmadani Raja, Roesman Ridwan Ramdan Sastra Ramdan Sastra Ramdaniah, Ramdaniah Rayner Alfred Rayner Alfred Resky Anugrah Rezky Anugrah Saiman, La Salim, Yulita Saly, Intan Novita Setyadi, Hario Jati Siti Rahmi Kelilauw St. Hajrah Mansyur Sugiarti, Sugiarti Sulfikar Sulfikar Sunarti Passura Backar Syafie, Lukman Syamsiar, Syamsiar Tasrif Hasanuddin Triyanna Widiyaningtyas Triyanna Widyaningtyas, Triyanna Umalekhoa, Alfi Syahrin Umar, Fitriyani Wahyuni Wahyuni Wd. Shaqina Rafa Naura Wistiani Astuti Wistiani Astuti Wong, Kelvin Wulan Purnama Sari Yudha Islami Sulistya Yulita Salim Yusrandi Yusrandi Zahif Safyin Saleh Zahirah, Dinna Zulkarnain, Nur Ainun