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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika Jurnal Teknologi Informasi dan Ilmu Komputer 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 JPM (Jurnal Pemberdayaan Masyarakat) 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 Jurnal Minfo Polgan (JMP) 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 The Indonesian Journal of Computer Science CSRID Linier: Literatur Informatika dan Komputer
Purnawansyah Purnawansyah
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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Perbandingan Metode Naïve Bayes dan K-NN dengan Ekstraksi Fitur GLCM pada Klasifikasi Daun Herbal A. Nurjulianty; Purnawansyah Purnawansyah; Herdianti Darwis
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6262

Abstract

Indonesia is a country with various types of herbal plants that have the potential to be very effective medicines. Herbal plants have been used since ancient times as natural medicines. One part that has health benefits is the leaves, however, there are many similarities between the different types of leaves. This research aims  to classify digital images of herbal leaves implementing the Naïve Bayes and K-Nearest Neighbor (KNN) methods with Gray Level Co-occurrence Matrix (GLCM) feature extraction. The dataset consisted of sauropus androgynus and moringa leaves with data collection in bright and dark scenarios. A total of 480 data which was divided into two parts, namely 80% for training data and 20% for testing images. The KNN distances used for comparison are Euclidean, Manhattan, Chebyshev, Minkowski, and Hamming. Meanwhile, Naïve Bayes uses Gaussian, Multinomial, and Bernoulli kernels. The results of the study showed that the KNN method with the Manhattan distance obtained the best results with an accuracy rate of up to 94% in bright scenarios.
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.
Klasifikasi Penyakit Tanaman Bawang Merah Menggunakan Convolutional Neural Network dan K-Nearest Neighbor Fifi Febrianti Usman; Purnawansyah; Herdianti Darwis; Erick Irawadi Alwi
Computer Science Research and Its Development Journal Vol. 15 No. 3 (2023): October 2023
Publisher : LPPM Universitas Potensi Utama

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

Abstract

The potential for yield loss due to shallot plant disease is the main trigger that can reduce agricultural productivity. Pest and disease attacks can be minimized and overcome quickly if farmers are able to classify the types of diseases that attack plants based on the characteristics and symptoms that appear. This study aims to classify shallot plant diseases, namely purple spotting and moles with a total of 320 datasets using Hue Saturation Value color feature extraction using the K-Nearest Neighbor (Euclidean Distance) and Convolutional Neural Network methods. Based on the results of the study, the accuracy, f1-score was 94% and precision, recal was 97%, 91% in purple spot disease while in moler disease it was 94% in accuracy, precision, recall, and f1-score in HSV and KNN classifications. Classification using HSV and CNN yielded high scores in accuracy, precision, recall, and f1-score with a value of 100% in both purple spot and moler shallot leaf diseases. Classification using deep learning CNN obtains very good accuracy, precision, recall and f1-score, namely 100%. With this description, the classification of shallot plant diseases using HSV and CNN, and CNN deep learning are stated to be able to classify shallot plant diseases, namely purple spotting and moles effectively and accurately.
Klasifikasi Citra Digital Daun Herbal Menggunakan Support Vector Machine dan Convolutional Neural Network dengan Fitur Fourier Descriptor Aulia Rezky Rahmadani Darmawati; Purnawansyah; Herdianti Darwis; Lutfi Budi Ilmawan
Computer Science Research and Its Development Journal Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

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

Abstract

Leaves are one component of plants that contain natural properties and are useful for maintaining human health. However, several types of leaves have the same characteristics and characteristics that make it difficult to distinguish. This study aims to classify types of herbal leaves using the SVM method with four kernels (Linear, RBF, Polynomial, Sigmoid) and CNN with Fourier descriptor (FD) feature extraction. The processed dataset is katuk leaf images, and Moringa leaf images of 480 images which are divided into 80% training data and 20% testing data using two scenarios, namely dark and light. From the testing process, it was found that FD + CNN in the light and dark scenarios obtained an accuracy value of 98%. Thus, the FD + SVM algorithm with Linear, RBF, polynomial kernels can be recommended in classifying herbal leaf images to have the best accuracy value of 100%.
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 study aimed 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 by using Canny Segmentation to create the edges of objects and clarify image details. The Hu Moments method, which describes the pixel distribution and shape of objects, was used to extract special features from images. Furthermore, classification modeling was carried out with Decision Tree and Random Forest to classify banknotes based on extracted characteristics. Model evaluation was carried out by measuring accuracy, precision, recall and f1-score performance and using cross-validation with k-fold = 5. The results showed that the Random Forest method was able to classify Rupiah banknotes well. In performance evaluation, the Random Forest method achieved an accuracy of 0.93 and good precision, recall, and f1-score scores for several banknote classes. The Decision Tree method also achieved good results, with an accuracy of 0.86. The results of the classification evaluation showed that the Random Forest method was better than the Decision Tree in classifying the 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 sentimen terhadap Body Shaming pada Twitter menggunakan Metode Naïve Bayes Classifier Fattah, St. Fajriah; Purnawansyah
Indonesian Journal of Data and Science Vol. 3 No. 2 (2022): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v3i2.46

Abstract

Salah satu bentuk media sosial yang sedang populer saat ini adalah twitter. Namun tidak jarang pengguna twitter memberikan komentar yang cenderung menyinggung pengguna twitter lain dengan kalimat negatif. Salah satu bentuk komentar negatif yang sering dilontarkan pengguna twitter adalah tentang body shaming. Body shaming merupakan komentar negatif terhadap fisik seseorang seperti gendut, pesek, cungkring dan lain-lain. Berdasarkan perilaku body shaming pada twitter, maka pada penelitian ini akan dilakukan analisis sentimen menggunakan metode Naive Bayes Classifier. Tujuan dari penelitian adalah mengukur performa accuracy, precision, recall, dan f-measure pada metode Naïve Bayes Classifier dalam analisis sentimen terhadap body shaming pada Twitter. Dataset tersebut digunakan untuk mengklasifikasikan tweets yang bersifat positif dan negatif. Teknik klasifikasi yang digunakan yaitu dengan mengukur performa dari accuracy, precision, recall, dan f-measure menggunakan metode naïve bayes classifier. Berdasarkan hasil pengujian performansi accuracy, precision, recall, dan f-measure dengan feature model trigram menggunakan metode naïve bayes classifier dilakukan pada dataset tweets body shaming yang berjumlah 908 data. Berdasarkan hasil pengujian performa dengan model trigram didapatkan hasil accuracy 61%, precision 56%, recall 55% dan f-measure 55%.
Brand Promotion Fakultas Ilmu Komputer Universitas Muslim Indonesia Andi Muhammad Naufal; Purnawansyah
Indonesian Journal of Data and Science Vol. 2 No. 2 (2021): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v2i2.50

Abstract

Brand adalah persepsi yang muncul pada audience mengenai sebuah perusahaan, figur ataupun ide (Hananto, 2019). Brand Identity adalah seperangkat asosiasi unik yang dimiliki oleh brand yang harus dibentuk dan dipelihara. Asosiasi ini merepresentasikan apa yang diwakili oleh brand dan mengimplikasikan janji kepada pelanggan dari organisasi (Fauziyah, 2016). Identitas merek (brand identity) dibutuhkan dalam membangun loyalitas yang nantinya akan memiliki nilai tersendiri yang mampu menjadi opini konsumen, yang bertujuan agar dapat lebih efektif dan berinteraksi sehingga membentuk persepsi dalam benak konsumen, dan menjadi dasar dari strategi promosi selanjutnya. Pemanfaatan brand identity diharapkan langkah optimasi media digital yang dikelola dengan baik serta dapat memberikan kualitas, Fakultas Ilmu Komputer (FIKOM UMI) diharapkan mampu memenuhi harapan konsumen agar memiliki reputasi yang baik, maka dari itu konsumen akan memiliki kepercayaan, menyukai, serta menganggap FIKOM UMI sebagai simbol representasi dari peradaban teknologi. Brand Identity merupakan bagian utama dari brand promotion. Penelitian ini bertujuan untuk merancang dan membangun brand identity untuk kebutuhan brand promotion sesuai dengan visi misi dan karakteristik dari FIKOM UMI. Hasil dari penelitian ini adalah sebuah media promosi yang terdiri dari media utama dan media pendukung. Media utama berupa logo dan aturan standarisasi penggunaan logo atau Graphic Standard Manual (GSM) Book, dan media pendukung seperti stationary kit, poster, banner, paper bag, billboard, backdrop, baju, dan media sosial.
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.
Co-Authors - Nurhikma A. Nurjulianty Abd. Rasyid Syamsuri Achmad Fanany Onnilita Gaffar Achmad Fanany Onnilita Gaffar Adela Regita Azzahra Adela Regita Azzahra Adnan, Adam Agung R Ahmad Fadly Aji P. Wibawa Aji Prasetya Wibawa Alfitriana Riska Alfitriana Riska Alfiyyah, Nurul Alisma, Alisma Andi Muhammad Adnan Rusdy Andi Muhammad Halis Andi Muhammad Naufal Andri Rajsya Anisatul Humairah Arfah, Jumrayanti Arrosied, Harun Arvina Yudithia Sompie Astuti, Wistiani Atussaliha, Nur Almar' Aulia Rezky Rahmadani Darmawati Awang Harsa Kridalaksana Awangga, Narendra Azwan Triyadi Backar, Sunarti Passura Basri, Haerunnisa Benny Leonard Enrico P Benny Leonard Enrico Panggabean Bustam, Faida Daeng Darmawati, Aulia Rezky Rahmadani Desi Anggreani Desy Indri Luthfia Andy Dewi Widyawati Dian Dian Dolly Indra Dwiyanto, Felix Andika Eka Arman Enrico Panggabean, Benny Leonard Erick Irawadi Alwi, Erick Irawadi Fahmi Fahmi Fajar AR, Muh. Aqil Faradibah, Amaliah Farniwati Fattah Fatimah Syarifuddin Fattah, Farniwati Fattah, St. Fajriah Fery Setyo Aji Fifi Febrianti Usman Firman Akbar Fitriyani Umar Gaffar, Achmad Fanany Onnlita Gaffar, Andi Widya Mufila Harlinda L Harlinda Lahuddin Hasihi, Cholisah Erman Hasnidar S. Hasrah Wahyuni Haviluddin Haviluddin Herawati Herawati Herman Herman Huzain Azis Ifan Wahyudi Irawati Irawati Irawati Irawati Iriani Indah Saputri Jumrayanti Arfah Kasmira Kasmira Kasmira, Kasmira Kotot Tri Hartanto Kurniati, Nia La Saiman Lilis Hayati lilis nurhayati Listyan Nur Saida Lokapitasari Belluano, Poetri Lestari Lukman Syafie Lutfi Budi Ilmawan, Lutfi Budi Lutfi Budiman Ilmuwan M. Imam Maulana M. Takdir Mahfuddin Mukmin Malani, Rheo Manga, Abdul Rachman 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 Nur Firdaus 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 Ainun Zulkarnain 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 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 Samir, Mutmainnah Sastra, Ramdan Setyadi, Hario Jati Siti Rahmi Kelilauw Sitti Nurhalimah Sitti Rahmah Jabir 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 Usman, Fifi Febrianti Wahyuni Wahyuni Wd. Shaqina Rafa Naura Widyawati, Dewi Wistiani Astuti Wistiani Astuti Wong, Kelvin Wulan Purnama Sari Yudha Islami Sulistya Yulita Salim Yusrandi Yusrandi Zahif Safyin Saleh Zahirah, Dinna Zulkifli Zulkifli