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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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Pengembangan Peningkatan Produktivitas dan Pemasaran UKM Abon Telur sebagai Oleh-Oleh Khas Malino di Desa Lonjoboko Kecamatan Parangloe Kabupaten Gowa Purnawansyah Purnawansyah; Dolly Indra; Lilis Nur Hayati; Fery Setyo Aji; Rezky Anugrah
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 14, No 1 (2023): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v14i1.5973

Abstract

Tujuan program pengabdian yang kami lakukan yaitu memberikan penyuluhan, dan simulasi tentang pemasaran produk, memberikan pelatihan bagi mitra desa Lonjoboko tentang kewirausahaan dalam memasarkan produk berbasis online dan mendesain kemasan yang menarik dan praktis. Metode dalam pelaksanaaan kegiatan ini adalah memfasilitasi dengan penyuluhan, simulasi dan pelatihan bagi para UKM dengan mewujudkan masyarakat sejahtera dan pandai dalam memasarkan produk dengan layanan sistem informasi berbasis online di desa Lonjoboko Kabupaten Gowa dalam bentuk pelatihan. Luarannya Mitra mendapatkan modul pelatihan manajemen kewirausahaan berbasis online untuk memasarkan produknya, mitra mampu mandiri dalam mengimplementasikan dan terampil dalam pemasaran produk, Software Aplikasi Web Sistem Informasi pemasaran produk.
Fourier Descriptor on Lontara Scripts Handwriting Recognition Fitriyani Umar; Herdianti Darwis; Purnawansyah Purnawansyah
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1040.193-200

Abstract

Hal yang kritis dalam proses pengenalan pola adalah ekstraksi fitur. Merupakan suatu metode untuk mendapatkan ciri-ciri suatu citra (image) sehingga dapat dikenali satu sama lain. Pada penelitian ini, metode deskriptor Fourier digunakan untuk mengekstraksi pola aksara Lontara yang terdiri dari 23 huruf. Deskriptor Fourier adalah metode yang digunakan dalam pengenalan objek dan pemrosesan citra untuk merepresentasikan bentuk batas segmen citra. Pengenalan karakter dilakukan dengan menggunakan jarak Euclidean dan Manhattan. Hasil pengujian menunjukkan bahwa tingkat pengenalan tertinggi mencapai akurasi 91,30% dengan menggunakan koefisien Fourier sebesar 50. Pengenalan huruf menggunakan Manhattan dan Euclidean cenderung sama atau menghasilkan akurasi yang cenderung serupa. Akurasi tertinggi dicapai saat menggunakan Manhattan sebesar 91,30%.
Asking a Chatbot for COVID-19 Food and Nutrition Fahmi Fahmi; Yusrandi Yusrandi; Aji P. Wibawa; Ming Foey Teng; Purnawansyah Purnawansyah
Bulletin of Culinary Art and Hospitality Vol. 1 No. 2 (2021)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (415.425 KB) | DOI: 10.17977/um069v1i22021p63-69

Abstract

Coronavirus 19 (COVID-19) is a disease caused by a new coronavirus called severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2; previously known as 2019-nCoV). On March 11, 2020, WHO declared COVID-19 a global pandemic, the first pandemic since H1N1 influenza was declared a pandemic in 2009. The disease has hit almost every country in the world and so far, no medicine nor antiviral was found. In some countries where the death rate is high, the number of patients continues to increase. On May 20, 2020, the number of patients exceeded 4.8 million and the toll is 318,000 (6.6 percent). Although the number of new patients in many countries declined after the access rights lockdown (lockdown), the second attack returned to the area where the first attack occurred. The COVID-19 case in Indonesia still shows an increasing trend even though various efforts have been made by the state and society. This research is intended to discuss us regarding the use of chatbots to provide guidance on food and nutrition during the COVID-19 pandemic. The results showed that a review of maintaining a healthy lifestyle by adhering to health protocol during the pandemic of COVID-19, shows that the higher the rating, the more effective it is to reduce the transmission of the corona virus in the future.
Sistem Pendukung Keputusan Menentukan Beras Unggulan pada Kabupaten Sidrap Menggunakan Metode Analytical Hierarchy Process Lilis Hayati; Purnawansyah Purnawansyah; Anisatul Humairah
Jurnal Inovasi Teknologi dan Edukasi Teknik Vol. 1 No. 9 (2021)
Publisher : Universitas Ngeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (538.099 KB)

Abstract

Application of a Decision Support System for Determining Superior Rice in Sidrap Regency Using a web-based Analytical Hierarchy Process (AHP) method can provide accurate, fast, easy, and precise ranking results for the Office of Agriculture in Sidrap Regency. From the calculation results obtained the application with the highest value is Super Slyp Rice with a value of 0.58. From the results of tests that have been carried out using the black box technique in beta testing, the highest percentage of questionnaires is 85 percent saying they agree that the application of the Decision Support System for Determining Superior Rice in Sidrap Regency Using the Analytical Hierarchy Process (AHP) method can be used and facilitates the determination of superior rice. at the Agriculture Service of Sidenreng Rappang Regency. Aplikasi Sistem Pendukung Keputusan Menentukan Beras Unggulan di Kabupaten Sidrap Menggunakan Metode Analytical Hierarchy Process (AHP) berbasis web dapat memberikan hasil perangkingan akurat, cepat, mudah dan tepat bagi Kantor Dinas Pertanian pada Kabupaten Sidrap. Dari hasil perhitungan aplikasi yang didapatkan dengan nilai tertinggi yaitu Beras Slyp Super dengan nilai 0.58. Dari hasil pengujian yang telah dilakukan dengan menggunakan teknik black box pada pengujian beta menghasilkan persentase tertinggi kuesioner yaitu sebanyak 85% mengatakan setuju bahwa aplikasi Sistem Pendukung Keputusan Menentukan Beras Unggulan di Kabupaten Sidrap Menggunakan Metode Analytical Hierarchy Process (AHP) dapat digunakan dan mempermudah penentuan beras unggulan di Dinas pertanian Kabupaten Sidenreng Rappang.
Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Support Vector Machine dengan Fitur Fourier Descriptor Putri Regina Prayoga; Purnawansyah Purnawansyah; Tasrif Hasanuddin; Herdianti Darwis
Jurnal Pendidikan Informatika (EDUMATIC) Vol 7 No 1 (2023): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v7i1.17521

Abstract

Indonesia is a rich country in herbal plants that can be used as traditional medicine. Leaves are one of the main components of herbal plants that are difficult to distinguish in texture and shape. This study aims to classify two types of herbal leaves, namely Sauropus androgynus and Moringa leaves using the K-nearest neighbor (KNN) and Support vector machine (SVM) with fourier descriptor (FD) feature extraction on texture and shape features. The research uses primary data collected through a smartphone camera as much as 480 image data with light and dark scenarios which are then divided into 80:20 training and testing data. Based on the research that has been done, it is found that the KNN for light scenario data and dark scenarios get 92% and 94% accuracy respectively. The test results using SVM with FD feature extraction obtain an accuracy of 96% for light and dark scenarios. Thus, SVM is more recommended in the classification of herbal leaf images.
K-Nearest Neighbor dan Convolutional Neural Network pada Klasifikasi Penyakit Tanaman Bawang Merah - Nurhikma; - Purnawansyah; Herdianti Darwis; Harlinda L
Techno.Com Vol 22, No 3 (2023): Agustus 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i3.8533

Abstract

Bawang merah merupakan suatu kebutuhan masyarakat terutama pada bahan makanan dan juga digunakan untuk Kesehatan. Dengan banyaknya manfaat bawang merah, dibalik itu juga memiliki suatu kendala atau resiko pada penanaman bawang merah salah satu resikonya adalah hama atau penyakit yang dapat merugikan petani bawang merah. Tujuan dari penelitian ini yaitu mengklasifikasi penyakit daun bercak ungu dan moler pada tanaman bawang merah, yang di implementasikan menggunakan metode ekstraksi fitur Gray Level Co-Occurance Matix (GLCM) yang digunakan untuk ekstraksi fitur tekstrur. Selain itu ada lima jarak yaitu Eucludiean, Manhattan, Chebyshev, Minkowski, Hamming digunakan dalam metode klasifikasi  K-Nearest Neighbor (KNN). Penelitian ini juga menggunakan metode klasifikasi Convolutional Neural Network (CNN). Hasil dari penelitian ini yang diperoleh menggunakan metode GLCM dan KNN dengan jarak Euclidean, Manhattan, Chebyshev, dan Minkowski mendapatkan hasil akurasi yang tinggi yakni sebesar 100%, sedangkan nilai akurasi terendah terdapat pada KNN jarak Hamming nilai akurasi yaitu sebesar 42%, adapun klasifikasi dari gabungan dari metode GLCM dan CNN mendapatkan hasil akurasi sebesar 100% dan pada metode CNN yang tanpa metode ekstraksi memiliki nilai akurasi sebesar 100%.
Pemanfaatan Microservice dengan GraphQL Federation Concept untuk Pengembangan Sistem Informasi Akademik (xSIA) Poetri Lestari Lokapitasari Belluano; Benny Leonard Enrico P; Purnawansyah Purnawansyah; Amaliah Faradibah; Rahmadani Rahmadani
Jurnal Inovasi Teknologi dan Edukasi Teknik Vol. 3 No. 1 (2023)
Publisher : Universitas Ngeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um068v3i12023p12–23

Abstract

Academic Information System (xSIA) is an application built to manage academic value transaction modules that make it easy for users to manage grades in online academic administration activities. The need for reconstruction of the xSIA microservice architecture from the previously built domain driven design model using the json (javascript object notation) data format, REST (Representational State Transfer and an architectural style for distributed hypermedia systems) communication protocol, authorization and authentication processes occur in each microservice , there is a pooling of data that is charged to the client which has caused the client to make many requests to the various available microservices, as well as making documentation if there are additional microservices. The xSIA system reconstruction was developed by changing the xSIA microservice architecture so that the concept of responsibility authorization and authentication can be carried out according to service needs. The approach to reconstructing the microservice architecture in the xSIA application uses a new concept with the single gateway microservice model and is built using the GraphQL Federation to facilitate data communication between the backend and frontend of the application and can be implemented in various programming languages to minimize downtime when modification process occurs. The results of this study are the xSIA application on the study plan transaction module (krs) using the GraphQL Federation Concept with the single gateway microservice model so that authorization and authentication responsibilities can be carried out according to service requirements with a realtime average of 373.15 milliseconds. Sistem Informasi Akademik (xSIA) adalah aplikasi yang dibangun untuk mengelola modul transaksi nilai akademik yang memberikan kemudahan kepada pengguna mengelola nilai dalam kegiatan administrasi akademik secara online. Kebutuhan rekonstruksi arsitektur microservice xSIA dari model domain driven design yang dibangun sebelumnya menggunakan format data json (javascript object notation), protokol komunikasi REST (Representational State Transfer and an architectural style for distributed hypermedia systems), terjadi proses otorisasi dan otentikasi yang ada di setiap microservice, terdapat penyatuan data yang dibebankan kepada client telah menyebabkan client harus melakukan banyak request ke berbagai microservice yang tersedia, serta pembuatan dokumentasi jika ada penambahan microservice. Rekonstruksi sistem xSIA dikembangkan dengan mengubah arsitektur microservice xSIA sehingga konsep responsibility autorisasi dan autentifikasi dapat dilakukan sesuai dengan kebutuhan service. Pendekatan dalam melakukan rekontruksi arsitektur microservice pada aplikasi xSIA menggunakan konsep baru dengan model single gateway microservice (layanan satu gerbang) dan dibangun menggunakan GraphQL Federation untuk mempermudah komunikasi data antara backend dan frontend dari aplikasi, serta dapat diimplementasikan di berbagai Bahasa pemrograman sehingga meminimaliasir terjadinya downtime saat proses modifikasi terjadi. Hasil penelitian ini berupa aplikasi xSIA pada modul transaksi rencana studi (KRS) menggunakan GraphQL Federation Concept dengan model single gateway microservice sehingga responsibility autorisasi dan autentifikasi dapat dilakukan sesuai dengan kebutuhan service dengan rerata realtime 373.15 millisecond.
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.
Analisis Performa Metode Support Vector Regression (SVR) dalam Memprediksi Harga Bahan Sembako Nasional Huzain Azis; Purnawansyah Purnawansyah; Nirwana Nirwana; Felix Andika Dwiyanto
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1686.390-397

Abstract

Support Vector Regression (SVR) is a supervised learning algorithm to predict continuous variable values. The basic goal of the SVR algorithm is to find the most suitable decision line. SVR has been successfully applied to several issues in time series prediction. In this research, SVR is used to predict the price of staple commodity, which are constantly changing in price at any time due to several factors making it difficult for the public to get groceries that are easy to reach. National staple commodity data consisting of 17 commodities, including shallots, honan garlic, kating garlic, medium rice, premium rice, red cayenne peppers, curly red chilies, red chili peppers, meat of broiler chicken, beef hamstrings, granulated sugar, imported soybeans, bulk cooking oil, premium packaged cooking oil, simple packaged cooking oil, broiler chicken eggs, and wheat flour. With a data set for the last 3 years, including from January 1, 2020, to December 31, 2022. There are 3 variables in the data set, namely commodity, date, and price. This research divides the entire dataset into 80% training and 20% testing data. The results of this research show that SVR using the RBF kernel produces good forecasting accuracy for all datasets with an average Mean Square Error (MSE) training data of 6,005 while data testing is 6,062, Mean Absolute Deviation (MAD) of training data is 6,730 while data testing is 6.6831, Mean Absolute Percentage Error (MAPE) training data is 0.0148 while data testing is 0.0147, and Root Mean Squared Error (RMSE) training data is 7.772 while data testing is 7.746.
Comparative Study of Herbal Leaves Classification using Hybrid of GLCM-SVM and GLCM-CNN Purnawansyah Purnawansyah; Aji Prasetya Wibawa; Triyanna Widyaningtyas; Haviluddin Haviluddin; Cholisah Erman Hasihi; Ming Foey Teng; Herdianti Darwis
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1759.382-389

Abstract

Indonesia is a tropical country with a diverse range of plants that ancient people used for traditional medicines. However, the similarity in shape of the leaves became an obstacle to distinguishing them. Therefore, technological advancements are expected to help identify the herbal leaves to use them right on target according to their efficacy. In this research, image classification of katuk (Sauropus Androgynus) and kelor (Moringa Oleifera) leaves is applied using 3 different algorithms i.e hybrid of Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Support Vector Machine (SVM) implementing 4 kernels namely linear, RBF, polynomial, and sigmoid; hybrid of GLCM and Convolutional Neural Network (CNN); and pure CNN. A dataset of 480 images has been collected with 2 different scenarios, including bright and dark intensities. Based on the result, a hybrid of GLCM and SVM showed the highest accuracy of 96% in the dark intensity test using a linear kernel, while sigmoid obtained the lowest accuracy of 35%. On the other hand, it has been discovered that CNN obtained the highest performance in the bright intensity test with an accuracy of 98%. While in the dark intensity test, a hybrid of GLCM and CNN is superior, obtaining 96% accuracy. In conclusion, CNN is more powerful for image classification with bright intensity. For dark intensity images, both the hybrid of GLCM+SVM (linear) and the hybrid of GLCM+CNN are fairly recommended.
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