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All Journal Jurnal Sistem Komputer Techno.Com: Jurnal Teknologi Informasi JUITA : Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sains dan Informatika Matrix : Jurnal Manajemen Teknologi dan Informatika Jurnal Teknoinfo ILKOM Jurnal Ilmiah Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer INTEK: Informatika dan Teknologi Informasi Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terapan Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika Ekonomi Bisnis Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Didaktikum Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Infotech: Jurnal Informatika & Teknologi Journal of Engineering and Applied Technology KLIK: Kajian Ilmiah Informatika dan Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Algoritme Jurnal Mahasiswa Teknik Informatika Explore JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Arief Hermawan
Universitas Tekonologi Yogyakarta
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Other

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Pengenalan Potensi Racun dan Peningkatan Keamanan Pangan Dalam Jamur Menggunakan Convolutional Neural Network Ilham Rafiedhia Pramutighna; Arief Hermawan
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.v7i4.6372

Abstract

One promising advancement in the field of food agriculture is the cultivation of mushrooms. Mushrooms can be broadly classified into two groups: edible mushrooms and non-edible mushrooms. Edible mushrooms serve various purposes, including as food, medicine, and other applications, while non-edible ones can lead to poisoning. However, distinguishing between edible and non-edible mushrooms is a complex task. Even a slight error in selecting suitable mushrooms for consumption can have health repercussions for consumers. The progress in science and technology, particularly in digital image processing, aids in the classification of mushrooms. Image classification using Convolutional Neural Networks (CNNs) presents an alternative to address this issue. This research primarily focuses on identifying potential toxins in mushrooms using CNNs, aiming to contribute to a more efficient and accurate approach in classifying mushrooms fit for consumption. The results demonstrate that the model trained with data augmentation achieved the highest accuracy, with 96.53% for training data and 93.22% for validation data, accompanied by lower loss rates. This underscores that CNNs are an efficient and accurate approach in classifying mushrooms based on their genus. Furthermore, this study also discovered that parameters such as the number of epochs, batch size, optimizer, image size, and image augmentation influence the model training process.
Analisis Perbandingan Algoritma SVM, Naïve Bayes, dan Perceptron untuk Analisis Sentimen Ulasan Produk Tokopedia Muhammad Aulia; Arief Hermawan
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.v7i4.6839

Abstract

The rapid growth of the online market in Indonesia has changed the business landscape. Tokopedia, one of the leading E-commerce platforms, serves millions of users with a variety of products. In the fierce E-commerce competition, understanding customer reviews is very important. However, performing review analysis manually is a complex and time-consuming task. Sentiment analysis is needed to understand customer preferences, improve service quality, and maintain Tokopedia's competitiveness in the competitive E-commerce market. This study carried out a comparison between three algorithms, Support Vector Machine, Perceptron, and Multinomial Naïve Bayes to evaluate and determine the most effective and accurate algorithm in conducting sentiment analysis of product reviews on Tokopedia. The results of research using 2000 Tokopedia product review data show that Multinomial Naïve Bayes has the highest level of accuracy, reaching 84.00% and precision of 96.00%. Support Vector Machines has an accuracy rate of 80.00% and a precision value of 95.00%. Meanwhile, Perceptron provides 81.00% accuracy and 95.00% precision. Evaluation using the confusion matrix also indicates that Multinomial Naïve Bayes provides superior results with a truth level of 1011 for positive sentiment labels and 860 for negative sentiment labels. This research provides valuable insights regarding sentiment analysis of product reviews on Tokopedia, and the results can be a reference for further research exploring more innovative sentiment analysis methods or the application of technology to increase the efficiency of sentiment analysis in the context of E-commerce.
Implementasi Metode Naïve Bayes untuk Klasifikasi Penderita Penyakit Jantung Bowo Hirwono; Arief Hermawan; Donny Avianto
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 3 (2023): JULY-SEPTEMBER 2023
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i3.910

Abstract

Heart attack is a very serious heart disorder. This disorder occurs when the heart muscle does not get good blood flow. This condition will interfere with the function of the heart in flowing blood flow throughout the body. This study aims to develop a system capable of classifying people with heart disease using the Naïve Bayes method. Naïve Bayes is a method that works based on the probability that a person has a heart disease or not based on their medical record data. This algorithm is used with the aim of calculating the probability of a person suffering from heart disease based on their medical records. This data was obtained from the University of California Irvine Machine Learning website with a total of 303 datasets with 13 attributes. This research was conducted by dividing the data into 75% for training data and 25% for training data. The results of this study indicate that the Naïve Bayes algorithm used gives a fairly high accuracy value of 86.84%.
Application of Fuzzy Tsukamoto Method to Rainfall Prediction in Sleman Regency Rizky Diar Panuntun; Arief Hermawan
Jurnal Riset Informatika Vol. 5 No. 4 (2023): September 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i4.76

Abstract

The context of this research is that erratic rainfall can disrupt community activities, especially for traders who want to make sales. In addition, information about rainfall is also needed by farmers in determining planting patterns in order to get maximum yields. The purpose of this research is to be able to help farmers predict rainfall to get maximum crop yields. One method to be able to predict rainfall is Fuzzy Logic. This research will use the Tsukamoto Fuzzy method. In the research conducted this time, the author conducted monthly rainfall forecasting in Sleman Regency. Rainfall data in Sleman Regency from 2015 to 2022 will be used in this research. This research succeeded in getting a MAPE value of 49.31%. The result of this research is the highest monthly rainfall prediction in November, with a rainfall of 713.78 mm. At the same time, the lowest occurred in August, which amounted to 36.47 mm. This research only gets a MAPE value of 49.31%. So it can be concluded that the Tsukamoto fuzzy method cannot predict rainfall well.
Perbandingan Model Machine Learning dalam Analisis Sentimen Ulasan Pengunjung Keraton Yogyakarta pada Google Maps Cahyo Prakoso; Arief Hermawan
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1419

Abstract

The Yogyakarta Palace, as a center of Javanese culture rich in history, art and tradition, attracts world attention and is the subject of various tourist reviews. In the digital era, these reviews are important for managing and improving tourism services. In particular, information technologies such as Google Maps facilitate public participation by providing a platform for visitors to share their experiences through reviews. These reviews provide important feedback for managers and potential visitors, while driving service improvements and innovation. Therefore, this research conducts review sentiment analysis using a Machine Learning model which is used to understand visitors' positive and negative views and compares three popular models in sentiment analysis, namely Naive Bayes, Logistic Regression, and Support Vector Machine (SVM). Review data collection uses the web scraping method with Selenium, followed by a preprocessing stage which includes translation, cleaning, normalization, tokenizing, removing stopwords, and stemming to prepare the data for sentiment analysis. Labeling is done based on review ratings, with a threshold below 4 for negative sentiment and 4 or 5 for positive. Based on data processing and analysis, the Support Vector Machine (SVM) model was proven to be the best model with an accuracy of 87.12% and an F1-Score of 90.91%, followed by Logistic Regression and Naive Bayes
Perbandingan Algoritma Naïve Bayes dan K-Nearest Neighbor Pada Imbalace Class Dataset Penyakit Diabetes Muhammad Rousydi Hunafa; Arief Hermawan
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1486

Abstract

Diabetes is a global health concern due to its significant impact on public health. Managing this disease is crucial to prevent serious complications. Technological advancements, particularly in machine learning models, have opened new avenues in diabetes identification. This study compares the performance of the Naive Bayes and K-Nearest Neighbor (KNN) classification algorithms on an imbalanced diabetes dataset. The primary aim is to evaluate these algorithms' performance in predicting diabetes while considering class imbalance. Classification methods were applied to previously collected datasets. The research findings demonstrate that Naive Bayes with the SMOTE technique exhibited the best performance with an accuracy of 71.66%, followed by Naive Bayes without SMOTE (76.03%), and KNN with SMOTE (80.47%). Although KNN without SMOTE showed the highest accuracy (83.02%), Naive Bayes with SMOTE showcased a better balance between accuracy, precision, and recall. The utilization of the SMOTE technique improved Naive Bayes' performance by enhancing precision and recall, indicating its capability to address class imbalance in the diabetes dataset. This study offers insights into selecting the best algorithms and effective techniques for handling class imbalance to predict diabetes on imbalanced datasets.
Perbandingan Metode K-Nearest Neighbor dan Support Vector Machine Untuk Memprediksi Penerima Beasiswa Keringanan UKT Enggar Novianto; Arief Hermawan; Donny Avianto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6913

Abstract

Scholarships are financial assistance provided to individuals, pupils, or scholars to extend their education. These may be provided by government agencies or the colleges themselves to students. One component that ensures quality human resources is formal education. The purpose of scholarships is to help disadvantaged or underprivileged students. Scholarship providers usually give some consideration to the student's level of difficulty, such as parents' salary and number of siblings. Due to the large number of applications for relief scholarships and strict assessment criteria, not all students who apply can be accepted. Scholarship application selection officers often have difficulty determining which students are worthy of receiving a scholarship. While the quota for scholarship recipients for this study program is always limited, applications for student UKT relief scholarships continue to increase every semester. This application came from students with poor economic conditions. To select UKT relief scholarship application documents, you have to consider various criteria and use manual methods which are less effective and require more time to determine the results. This research aims to make a comparison between the K-Nearest Neighbor and Support Vector Machine classification algorithms in determining recipients of UKT relief scholarships for undergraduate students in the Legal Sciences Study Program, Faculty of Law, Sebelas Maret University using the RapidMiner application. The accuracy results obtained using the RapidMiner application that have been carried out, the K-NN method produces an accuracy of 92.92%, while the SVM method produces an accuracy of 85.84%, so the K-NN method is the best method in classification for predicting recipients of UKT relief scholarships for students in the program. Bachelor of Law studies.
Analisis sentimen twitter terhadap pemilihan presiden menggunakan algoritma naïve bayes Panji Al Muqsith Prasetyo; Arief Hermawan
INFOTECH : Jurnal Informatika & Teknologi Vol 4 No 2 (2023): INFOTECH: Jurnal Informatika & Teknologi
Publisher : LPPMPK - Sekolah Tinggi Teknologi Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/infotech.v4i2.863

Abstract

Tahun 2024 esok merupakan akhir dari masa kepemimpinan presiden Joko Widodo yang mana menjadi penentu dari presiden selanjutnya. Saat ini masyarakat indonesia sedang heboh dengan maraknya calon presiden dan calon wakil presiden yang sudah ditentukan. Capres-cawapres saat ini yang dapat dipastikan akan berkompetisi di ajang pemilihan presiden antara lain Ganjar-Mahfud, Prabowo-Gibran, dan Anies-Cak imin. Berbagai komentar dari banyaknya platform internet seperti sosial media khususnya media sosial Twitter atau X menjadi tempat menuai opini dan reaksi terhadap ketiga capres-cawapres tersebut. Ini menjadi masalah bagi masyarakat yang kurang literasi dalam kebahasaan dan literasi digital, mereka akan mudah terpancing serta tergiring oleh opini dari netizen yang memiliki literatur lebih baik. Dengan munculnya masalah tersebut, maka dikembangkan sistem sentimen analisis ini yang menjadi salah satu platform untuk menentukan memiliki sifat apa komentar tersebut secara otomatis. Sifat dari komentar umumnya dibagi menjadi 3, yaitu netral, positif, dan negatif. Ditambah, dengan metode crawling menggunakan API dari X (Twitter) akan mempermudah dalam mengumpulkan dataset komentar dengan lebih fleksibel. Dari hasil crawling ini, didapat komentar sebanyak 540. Metode lain yang akan digunakan untuk proses sentimen analisis ini yaitu algoritma naive bayes dengan tipe multinomialNB. Penelitian ini menghasilkan nilai akurasi sebesar 85% dengan precision 86.54%, recall 85%, dan f1-score sebesar 85% dimana hasil ini menggunakan skenario test_size sebesar 0.2 yang menjadi skenario paling baik dalam pembangunan model
Identifikasi Jejak Macan dan Anjing Menggunakan Momen Invarian dan Jaringan Saraf Tiruan Backpropagation Muhammad Zakariyah; Arief Hermawan
Explore Vol 12 No 1 (2022): Januari 2022
Publisher : Universitas Teknologi Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35200/ex.v12i1.86

Abstract

Identifikasi binatang berfungsi untuk mengetahui keberadaan dan status populasinya. Proses identifikasi dapat dilakukan melalui jejak kaki yang ditinggalkan. Beberapa jenis binatang memiliki kemiripan pola jejak kaki, sehingga cukup sulit untuk membedakannya. Jejak kaki macan dan anjing memiliki kemiripan yang bagi masyarakat awam akan kesulitan untuk membedakan kedua jenis binatang tersebut. Terlebih lagi macan merupakan jenis binatang buas yang membahayakan bagi masyarakat, terutama masyarakat yang tinggal di daerah hutan. Untuk membedakan kedua jenis binatang tersebut, dilakukan dengan mengenali pola jejak kaki yang ditinggalkan di tanah, pasir, dan lumpur. Data yang digunakan dalam penelitian ini berjumlah 34 gambar, dengan masing-masing 17 gambar untuk jejak macan dan anjing. Pengenalan pola/fitur jejak kaki macan dan anjing memanfaatkan momen invarian dan jaringan saraf tiruan backpropagation. Momen invarian digunakan untuk mengekstraksi fitur yang dimiliki oleh gambar jejak kaki macan dan anjing, sedangkan backpropagation digunakan untuk mengklasifikasikan jenis jejak tersebut. Hasil penelitian menunjukkan bahwa ekstraksi fitur dengan menggunakan momen invarian bernilai tetap terhadap perubahan posisi dan penskalaan. Metode backpropagation yang digunakan untuk identifikasi jejak macan dan anjing, memberikan persentase pengenalan sebesar 100% terhadap 10 data yang dilatihkan, dan mencapai 54,17% terhadap 24 data non-latih (data uji). Hal ini menujukkan bahwa sistem dapat mengidentifikasi macan dan anjing berdasarkan jejak kaki yang ditinggalkan.
Optimization of Hyperparameter K in K-Nearest Neighbor Using Particle Swarm Optimization Muhammad Rizki; Arief Hermawan; Donny Avianto
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.20688

Abstract

This study aims to enhance the performance of the K-Nearest Neighbors (KNN) algorithm by optimizing the hyperparameter K using the Particle Swarm Optimization (PSO) algorithm. In contrast to prior research, which typically focuses on a single dataset, this study seeks to demonstrate that PSO can effectively optimize KNN hyperparameters across diverse datasets. Three datasets from different domains are utilized: Iris, Wine, and Breast Cancer, each featuring distinct classification types and classes. Furthermore, this research endeavors to establish that PSO can operate optimally with both Manhattan and Euclidean distance metrics. Prior to optimization, experiments with default K values (3, 5, and 7) were conducted to observe KNN behavior on each dataset. Initial results reveal stable accuracy in the iris dataset, while the wine and breast cancer datasets exhibit a decrease in accuracy at K=3, attributed to attribute complexity. The hyperparameter K optimization process with PSO yields a significant increase in accuracy, particularly in the wine dataset, where accuracy improves by 6.28% with the Manhattan matrix. The enhanced accuracy in the optimized KNN algorithm demonstrates the effectiveness of PSO in overcoming KNN constraints. Although the accuracy increase for the iris dataset is not as pronounced, this research provides insight that optimizing the hyperparameter K can yield positive results, even for datasets with initially good performance. A recommendation for future research is to conduct similar experiments with different algorithms, such as Support Vector Machine or Random Forest, to further evaluate PSO's ability to optimize the iris, wine, and breast cancer datasets.
Co-Authors Achmad Safruddin Aditya Dwi Putro Adityo Permana Adityo Permana Wibowo Akmal Setiawan Wijaya Aldi Tri Wijaya Arrosyid, Muhammad Zydane Bagus Wibowo Kusumo Baiq Nurul Azmi Bowo Hirwono Cahyo Prakoso Donny Avianto Edi Priyanto Enggar Novianto Ferdinandus Edwin Penalun Gunawan Wibisono Ilham Rafiedhia Pramutighna Imantoko Imantoko Intan Setiawati M. Royani Sopia Al Hasan Muh Arifandi Muhammad Aulia Muhammad Rizki Muhammad Rizki Muhammad Rousydi Hunafa Nur Hidayati Nur Widiastuti Nurcakhyadi, Fredianto Panji Al Muqsith Prasetyo Reski Noviana Rian Oktafiani Rianto Rianto Rizky Diar Panuntun Saifullah, Shoffan Shoffan Saifullah Sutarman . Tri Waluyo Tri Widodo Triyas Hevianto Saputro Wardaya, Imanuel Puspa