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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
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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APPLICATION OF GROUP DECISION MAKING IN DETERMINING CULINARY TOURISM WITH TOPSIS AND BORDA METHODS Wd. Shaqina Rafa Naura; St. Hajrah Mansyur; Purnawansyah Purnawansyah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5017

Abstract

Makassar City is one of the destination cities for traveling. Makassar City offers a variety of interesting tours, one of which is culinary tourism. The determination of the best culinary tourism is based on the criteria set by the Makassar City Tourism Office. In managing culinary destinations, tourists are often faced with many choices, so they are confused about choosing the most attractive culinary destinations. This research uses the TOPSIS and BORDA methods. The TOPSIS method is used in determining culinary tourism alternatives based on criteria that become recommendations and the BORDA method is used in determining the selected alternatives based on several DMs who evaluate alternatives. The main objective of this research is to apply group decision making in selecting the best culinary tourism destinations in Makassar City based on group preferences and related criteria with TOPSIS and BORDA methods. This research has conducted 5 iterations involving 4 DMs from the Makassar City Tourism Office. Based on the results of the interview, 8 criteria and 35 alternatives were obtained. Where the Coto Nusantara alternative is ranked the highest with a value of 109,949. While Sop Saudara Irian is ranked last with a value of 62,896. The general benefit of this research is the application of group decision making in determining culinary tourism with the TOPSIS and BORDA methods can produce more objective and representative decision results. This can increase tourist satisfaction in determining culinary tourism.
Multiclass Classification of Rupiah Banknotes Based on Image Processing Azis, Huzain; Purnawansyah, Purnawansyah; Alfiyyah, Nurul
ILKOM Jurnal Ilmiah Vol 16, No 1 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i1.1784.87-99

Abstract

This research aims to classify the nominal value of Rupiah banknotes using image processing and classification methods. The research design was conducted by collecting a dataset of Rupiah banknotes consisting of 30 classes, each with 100 images. This research uses image preprocessing using Canny Segmentation to create object edges and clarify image details. The Hu Moments method, which describes pixel distribution and object shape, is used to extract special features from the image. Classification modeling is then performed using Decision Tree and Random Forest to classify banknotes based on the extracted characteristics. Model evaluation is performed by measuring accuracy, precision, recall, and f1socre performance and using cross-validation with k-fold=5. The results show that the Decision Tree method is able to classify Rupiah banknotes well. In the performance evaluation, the Decision Tree method achieved the highest accuracy of 86.83% and good precision, recall, and f1-score for several banknote classes. The Random Forest method also achieved good results, with the highest accuracy of 78.67%. The classification evaluation results show that the Decision Tree method is better than the Random forest in classifying Rupiah banknotes.
An Analysis of Classification Method Performance on Handwritten Lontara Numerals Bustam, Faida Daeng; Purnawansyah, Purnawansyah; Azis, Huzain
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.11999

Abstract

The research investigates the performance of various classification methods on handwritten Lontara digits, a script used by the Bugis and Makassar communities in South Sulawesi, Indonesia. The dataset comprises 10,890 samples from 99 individuals, categorized into 10 classes (digits 0-9). The study employs the K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Nu-Support Vector Classifier (NuSVC) algorithms, implementing cross-validation to assess accuracy, precision, recall, and F1 score. The results indicate varying performance across classifiers, with GNB showing the highest recall, while KNN and NuSVC display moderate effectiveness. The study concludes with recommendations for further improving classification accuracy through enhanced feature extraction and algorithm optimization.
Comparative Performance Evaluation of Classification Methods for Arabic Numeral Handwritten Recognition Saly, Intan Novita; Purnawansyah, Purnawansyah; Azis, Huzain
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.11998

Abstract

This study aims to evaluate the performance of various classification methods in recognizing handwritten Arabic numerals, particularly the K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and NU Support Vector Classifier (NU SVC) algorithms. In this study, a dataset of handwritten Arabic numerals consisting of 9,350 samples with 10 different classes was used. The research process involved data collection, data labeling, dividing the dataset into training and testing data, implementing classification algorithms, and performance testing using cross-validation methods. The results showed that NU SVC had more stable performance with accuracy close to KNN, while GNB showed the lowest performance. The conclusion of this study emphasizes that the selection of algorithms and parameter optimization is crucial to improve the accuracy and efficiency of handwriting recognition systems. Support Vector Machine (SVM) based algorithms proved to be superior in handling complex classification tasks compared to GNB. This study provides significant contributions to the field of handwriting recognition, particularly in the context of Arabic numeral handwriting, and can serve as a reference for developers of optical character recognition (OCR) systems in the future. Future research is recommended to increase the variety of datasets and further explore parameter optimization and data preprocessing techniques to improve system accuracy.
Analisis Eksplorasi Data Aplikasi Android pada Playstore Munaf, Adryan Dwiprawira; Purnawansyah, Purnawansyah; Darwis, Herdianti
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 4, No 4 (2023)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v4i4.1847

Abstract

Google Playstore memiliki karakteristik yang berbeda dengan Apple App Store yaitu lebih terbuka terhadap developer aplikasi mobile sehingga memiliki varian yang lebih beragam dibanding dengan Apple App Store. Setiap aplikasi di dalam app store dapat dikelompokan berdasarkan karakteristik yang sama dan dapat disebut sebagai kategori dan genre. Pada tahun 2018 jumlah mobile app yang tersedia mencapai 3,6 juta aplikasi. Berbagai jenis mobile app tersedia pada layanan google play store, mulai dari hiburan, media sosial, editor, jasa transportasi, perdagangan (marketplace), dan kesehatan. Penelitian ini bertujuan untuk melakukan 5 analisis yaitu aplikasi dengan rating tertinggi, mencari 5 aplikasi dengan size paling berat (MBs), visualisasi data content ratings aplikasi, mengidentifikasi aplikasi dengan install terbanyak, visualisasi kategori aplikasi. Dari 2152981 data yang telah di crawling diperoleh bahwa 5 aplikasi dengan rating tertinggi yaitu Biliyor Musun - Sonsuz Yarış, CoronaSurveys, Amkshoproom Shopping, Merlin CRM, Tictactoe Superpowers dan free game. Fun and Chalmo, mencari 5 aplikasi dengan size paling berat (MBs) yaitu SkySafari 6 Pro, Audio Book Bible Offline Arabic, Audio Book Bible Offline Burmese, Audio Book Bible Offline Amharic dan Audio Book Bible Offline Germany, Visualisasi content data rating dari grafik dapat kita lihat bahwa mayoritas aplikasi mobile pada android mengatur content rating kedalam kategori Everyone, Mengindentifikasi aplikasi install terbanyak dari data yang telah diperoleh bahwa hanya terdapat 1 aplikasi yang memiliki jumlah install lebih dari 10M install dan 14 aplikasi yang memiliki jumlah install lebih dari 5 M, visualisasi kategori aplikasi dari data yang diperoleh bahwa aplikasi berkategori education memiliki jumlah terbanyak yang ada di pasar playstore saat ini.
Implementasi Metode Technique For Order Preference by Similarity to Ideal Solution Dalam Penentuan Lokasi Penanaman Bawang Merah. Zulkarnain, Nur Ainun; Purnawansyah, Purnawansyah; Ramdaniah, Ramdaniah
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 4, No 2 (2023)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v4i2.1648

Abstract

Kabupaten Enrekang di Sulawesi Selatan memiliki potensi besar untuk budidaya bawang merah, yang menjadi komoditas utama di Kecamatan Anggeraja. Namun, beberapa petani menghadapi kesulitan dalam menemukan lokasi penanaman yang optimal. Oleh karena itu, penelitian ini mengusulkan penggunaan aplikasi penentuan lokasi penanaman bawang merah sebagai solusi. Tujuannya adalah mempermudah petani, terutama di Kecamatan Anggeraja, Kabupaten Enrekang, dalam memilih lahan yang cocok untuk budidaya bawang merah. Dalam penelitian ini, metode Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) digunakan untuk mengevaluasi 15 alternatif lahan pada aplikasi tersebut. Sistem ini membantu pengguna, khususnya petani, dalam mencari dan memilih lahan yang sesuai dengan kriteria yang ditetapkan, sehingga mendapatkan rekomendasi lahan yang optimal. Hasil penelitian menunjukkan kecocokan metode TOPSIS dalam sistem, dengan tingkat akurasi presentasi sebesar 85% dari 15 alternatif yang diuji.
METODE SUPPORT VECTOR MACHINE UNTUK KLASIFIKASI DATA PENYAKIT HATI YANG IMBALANCE Rahmah, Nur; Purnawansyah, Purnawansyah; Umar, Fitriyani
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 5, No 1 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v5i1.2189

Abstract

Penelitian ini bertujuan untuk melakukan klasifikasi penyakit hati menggunakan metode Support Vector Machine (SVM) dan Synthetic Minority Oversampling Technique (SMOTE). Data yang digunakan berupa data sekunder yang diperoleh dari situs Kaggle dengan jumlah data sebanyak 582 sampel. Data tersebut terdiri dari 10 fitur yang digunakan sebagai variabel masukan SVM. Proses klasifikasi dilakukan dengan membagi data menjadi data training 70% dan data testing 30%. Hasil dari penelitian yang telah dilakukan ialah dengan menggunakan metode support vector machine mampu melakukan klasifikasi data penyakit hati dengan hasil klasifikasi yang menunjukkan nilai 0 dan 1. Dimana nilai 0 menandakan bahwa pasien tersebut tidak mengidap penyakit hati dan nilai 1 menandakan bahwa pasien tersebut mengidap penyakit hati. Berdasarkan proses klasifikasi data penyakit hati yang telah dilakukan memperoleh nilai akurasi performansi yaitu 67,06%, dan berdasarkan proses visualisasi data yang telah dilakukan dalam proses pengklasifikasian data tersebut ditemukan ketidakseimbangan data penyakit hati. Ketidakseimbangan data yang peroleh dilakukan oversampling menggunakan metode SMOTE untuk menyeimbangkan data. Penelitian telah melakukan proses penyeimbangan data penyakit hati sehingga tenaga menis lebih terbantukan dalam mendeteksi penyakit hati yang diderita oleh pasien
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.
Co-Authors - Nurhikma A. Nurjulianty Abd. Rasyid Syamsuri 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 Anggreani, Desi 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 Benny Leonard Enrico Panggabean Bustam, Faida Daeng Cholisah Erman Hasihi Darwis, Herdianti Desi Anggreani Dewi Widyawati Dian Dolly Indra Fahmi Fahmi Faradibah, Amaliah Farniwati Fattah Fatimah Syarifuddin Fattah, Farniwati Felix Andika Dwiyanto Fery Setyo Aji Fitriyani Umar Harlinda L Harlinda Lahuddin Hasnidar S. Haviluddin Haviluddin Herdianti Darwis Herman Herman Huzain Azis Ifan Wahyudi Inggrianti Pratiwi Putri Irawati Irawati Irawati Irawati Iriani Indah Saputri Jumrayanti Arfah Kasmira Kasmira La Saiman Lilis Hayati lilis nurhayati Listyan Nur Saida Lokapitasari Belluano, Poetri Lestari Lukman Syafie M. Imam Maulana M. Takdir Mahfuddin Mukmin Malani, Rheo Manga, Abdul Rachman Mansyur, St. Hajrah Mardiyyah Hasnawi Ming Foey Teng Muh Alim Abdi Muhammad Arfah Asis 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 Rahmadani Rahmadani Raja, Roesman Ridwan Ramdan Sastra Ramdan Sastra Ramdaniah, Ramdaniah Rayner Alfred Rayner Alfred Resky Anugrah Rezky Anugrah Salim, Yulita Saly, Intan Novita Setyadi, Hario Jati St. Hajrah Mansyur Sugiarti, Sugiarti Sulfikar Sulfikar Sunarti Passura Backar Syafie, Lukman Syamsiar, Syamsiar Tasrif Hasanuddin Triyanna Widiyaningtyas Triyanna Widyaningtyas Triyanna Widyaningtyas, Triyanna Umar, Fitriyani Wahyuni Wahyuni Wd. Shaqina Rafa Naura Wistiani Astuti Wistiani Astuti Wong, Kelvin Yudha Islami Sulistya Yulita Salim Yusrandi Yusrandi Zahif Safyin Saleh Zahirah, Dinna Zulkarnain, Nur Ainun