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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences Jurnal Ilmu Komputer Jurnal Sains dan Teknologi EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnas Nasional Teknologi dan Sistem Informasi Infotech Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Jurnal Pilar Nusa Mandiri JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Tekno Insentif Indonesian Journal of Electrical Engineering and Computer Science JIKA (Jurnal Informatika) Jurnal Responsif : Riset Sains dan Informatika Djtechno: Jurnal Teknologi Informasi Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Sistem Informasi dan Ilmu Komputer Jurnal Informatika Polinema (JIP)
Toni Arifin
Universitas Adhirajasa Reswara Sanjaya
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Neuroscience Physics Social Sciences Transportation

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Implementasi Algoritma K-Means Dalam Pembelian NFT Pada Jaringan Solana Muhamad Revin Reginal Harahap; Toni Arifin
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 6, No 4 (2023): Agustus 2023
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v6i4.6486

Abstract

Abstrak - Solana merupakan mata uang kripto yang dibuat dengan inovasi skala web pada jaringan blockchain dan memberikan aplikasi perjanjian cerdas yang terdesentralisasi. Banyak hal yang bisa dilakukan di platform blockchain solana, salah satunya dengan membeli NFT pada marketplace nya. NFT merupakan istilahtoken cryptocurrency yang tidak dapat dipertukarkan seperti koin biasa. Pasar NFT diperdagangkan secara elektronik. Permasalahan yang biasanya muncul adalah FOMO atau “Fear of Missing Out” ketika investor baru mulai mendengar tentang kondisi pasar saat ini, mereka cenderung khawatir bahwa sudah terlambat untuk bertindak hal ini karena ingin mencari keuntungan dengan cepat tanpa melakukan analisa untuk menyadari potensi kegunaanya untuk jangka pendek maupun jangka panjang. Untuk itu dibutuhkan suatu analisis informasi dari data produk yang dijualdi pasar NFT agar dapat membandingkan suatu NFT sebelum membelinya, yaitu dengan data mining. Penelitian ini membahas tentang teknik data mining yaitu Clustering dengan algoritma K-Means untuk mengelompokan data NFT. Dataset ini diambil melalui platform Magic Eden dengan nama popular collection dengan jumlah data sebanyak 419 record, dan terdiri dari 5 atribut. Dengan menggunakan aplikasi Rapidminer data diolah menggunakan K-Means sehingga diperoleh hasil pengelompokkan data pada cluster 0 tediri 389 items dengan total supply 3.355,853,total volume 12.068.821, floor price 4,936, dan 1.178,519 total owners. Cluster 1 terdiri dari 3 items dengan total supply 8.336,333, total volume 1.261.475,077, floor price 98,6667, dan 3.964,667 total owners. Pada cluster 2 terdiri dari 2 items dengan total supply 10,000, total volume 621.531,570, floor price 130,500, dan 6.931 total owners. Pada cluster 3 terdiri dari 1 items dengan total supply 10.000, total volume 1.908.911.49, floor price 95, dan 5.597 total owners. Pada cluster 4 terdiri dari 24 items dengan total supply 6.791,25, total volume 206.010,754, floorprice 28,625, dan 3.320,125 total owners.Kata Kunci: Data mining, K-Means Clustering, NFT, BlockchainAbstract - Solana is a cryptocurrency created with web-scale innovation on a blockchain network and provides decentralized innovative agreement applications. Many things can be done on the Solana blockchain platform, one of which is by buying NFT in the marketplace. NFT is a cryptocurrency token term that cannot be exchanged like ordinary coins. NFT market electronically. The problem that usually arises is FOMO or “Fear of Missing Out” When investors are just starting to hear about current market conditions, they tend to worry that it is too late to act because they want to find profits quickly without analyzing to realize the potential benefits for the short and long term. long-term. For that, we need an analysis of information from product data sold in the NFT market to be able to compare an NFT before, namely with data mining. This study discusses data mining techniques, namely Clustering with the K-Means algorithm to group NFT data. This dataset was taken through the Magic Eden platform under the name popular collection with a total of 419 records and consists of 5 attributes. By using rapid miner data which is processed using K-Means so that the results of data grouping in cluster 0 consist of 389 items with a total supply of 3,355,853, a total volume of 12,068,821, a base price of 4,936, and 1,178,519 real owners. Cluster 1 consists of 3 items with a total collection of 8,336,333, a total volume of 1,261,475,077, a floor price of 98,6667, and a total owner of 3,964,667. Cluster 2 consists of 2 items with a total supply of 10,000, a total volume of 621,531,570, a floor price of 130,500, and 6,931 total owners. Cluster 3 consists of 1 item with a total supply of 10,000, a total volume of 1,908,911.49, a floor price of 95, and 5,597 total owners. Cluster 4 consists of 24 items with a total collection of 6,791.25, a total volume of 206,010.754, a floor price of 28,625, and a total owner of 3,320,125Keywords: Data mining, K-Means Clustering, NFT, Blockchain
Optimasi Decision Tree Menggunakan Particle Swarm Optimization untuk Klasifikasi Kesuburan pada Pria Tyas Widyani Pratiwi; Toni Arifin
Sistemasi: Jurnal Sistem Informasi Vol 10, No 1 (2021): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (518.218 KB) | DOI: 10.32520/stmsi.v10i1.967

Abstract

AbstrakKeturunan adalah hal yang sangat diharapkan pada setiap pasangan suami istri, maka dari itu tingkat kesuburan pada pria adalah salah satu faktor penting. Faktor yang pempengaruhi tingkat kesuburan itu sendiri seperti hormon, penyakit bawaan, pernah atau tidaknya dioperasi. Salah satu cara untuk menganalisis kesuburan pada pria dapat dilakukan dengan teknik data mining. Data Mining dapat digunakan untuk mengklasifikasi ataupun prediksi. Penelitian ini, bertujuan untuk mendapatkan metode klasifikasi terbaik yang dapat menghasilkan tingkat dari nilai akurasi yang tinggi bila dikombinasikan dengan metode Particle Swarm Optimization (PSO). Pada penelitian ini, algoritma Decision Tree dengan Particle Swarm Optimization meningkatkan nilai akurasi sebesar 93.33% dan nilai AUC sebesar 0,793 termasuk kedalam kategori Fair classification.Kata Kunci: data mining, decision tree, kesuburan, particle swarm optimization AbstractHeredity is something that is expected in every married couple, therefore the level of fertility in men is one important factor. Factors that influence the level of fertility itself such as hormones, congenital diseases, surgery or not. One way to analyze fertility in men can be done with data mining techniques. Data Mining can be used to classify or predict. This study aims to obtain the best classification method that can produce a high level of accuracy when combined with the Particle Swarm Optimization (PSO) method. In this study, the Decision Tree algorithm with Particle Swarm Optimization increased the accuracy value by 93.33% and the AUC value by 0.793 was included in the Fair classification category.Keywords: data mining, decision tree, fertility, particle swarm optimization
Implementasi Algoritma K-Nearest Neighbor Untuk Klasifikasi Penerimaan Beasiswa Program Indonesia Pintar Chusi Yanasari; Toni Arifin
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 1 No. 4 (2023): November : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : Universitas Katolik Widya Karya Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v1i4.1862

Abstract

Scholarships are a form of assistance in the form of educational expenses provided by the government or foundations to students or students who are categorized as from underprivileged families. However, in datermining scholarship recipients, there are still many scholarship recipients who come from wealthy families, while those from less fortunate families do not receive this assistance. This may be due to calculations and data processing that still use manual methods, causing scholarship recipients to not be on target. The purpose of this research is to simplify and minimize calculation errors in determining scholarship recipients for the Smart Indonesia Program (PIP) at SMK Karya Medika. Therefore, for calculating and processing PIP scholarship recipients data, data mining techniques can use the calssification method using the K-NN algprithm. K-Nearest Neighbor is a data classification method that will be used for data objects based on learning data that is closer to the object. In this study using the Confusion Matrix test so as to obtain an accuracy value of 80.00%.
Penerapan Metode Naïve Bayes Dalam Mendiagnosa Kerusakan Printer Tiki Ramdhani; Toni Arifin
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 3 No. 3 (2023): November : Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v3i3.618

Abstract

Printers are devices that are utilized as print media. Despite the fact that a lot of printers have issues, little is known about printer damage at this time. while damage necessitates careful handling. The Naive Bayes method is one of many methods that can be used in expert systems. The point of this examination is to figure out how to carry out the utilization of diagnosing printer harm utilizing the Guileless Bayes strategy at the TR PrintComp organization and to figure out what the test aftereffects of the application are in diagnosing printer harm utilizing the Gullible Bayes technique at the TR PrintComp organization. The exploration strategy involved is writing concentrate on in this examination bringing about an application for diagnosing harm to printers utilizing the Credulous Bayes technique. Black box testing and confusion matrix methods were used for testing. The consequences of this exploration show that scientists have prevailed with regards to making a specialist framework for diagnosing printer harm utilizing the Gullible Bayes technique utilizing the PHP and MySQL programming dialects as a data set. Based on expert system testing, the Naive Bayes method was used to accurately diagnose printer damage with a 72.4% success rate. It is anticipated that this expert system application will offer a different approach to dealing with printer damage issues. The result from this master framework is as data or data in regards to the sort of printer harm and is joined by arrangements and ways of defeating them. The author's implementation of the Naive Bayes method for diagnosing printer damage is able to quickly identify the kind of damage.
OPTIMALISASI ALGORITMA TERJEMAHAN BAHASA DENGAN MODEL TRANSFORMER: PENDEKATAN STATISTICAL MACHINE LEARNING Maksum, Muhamad Shidiq; Arifin, Toni; Rohidin, Rifki; Prasetya, Muhamad Azril Budi; Anshori, Iedam Fardian
INFOTECH journal Vol. 10 No. 2 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v10i2.11132

Abstract

Di era globalisasi ini perkembangan teknologi sangat pesat, berbagai macam bentuk teknologi hadir untuk memudahkan pekerjaan manusia. Diantara sekian banyak teknologi yang ada, salah satu teknologi yang banyak digunakan adalah Aplikasi Penerjemah. Aplikasi Penerjemah memudahkan penggunanya dalam berkomunikasi antar negara, maupun antar wilayah di suatu negara, salah satunya Indonesia. Indonesia merupakan negara yang kaya akan keberagaman, termasuk bahasa. Indonesia mempunyai 700 bahasa daerah. Penelitian ini bertujuan untuk mengoptimalkan algoritma aplikasi penerjemah bahasa daerah di Indonesia. Metode yang digunakan dalam penelitian ini adalah pengumpulan data, pra-pemrosesan data, pembagian data, pembuatan model, dan pelatihan model. Hasil yang diharapkan dari optimalisasi aplikasi ini adalah aplikasi ini dapat berfungsi membantu masyarakat Indonesia berkomunikasi satu sama lain dengan baik. Juga membuat masyarakat semakin mencintai bahasa Indonesia.
RANCANGAN VIRTUAL PRIVATE NETWORK PADA KANTOR PROLOV MENGGUNAKAN ZEROTIER Suhadi, Enda; Arifin, Toni
Jurnal Informatika Vol 8, No 1 (2024): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v8i1.9979

Abstract

Pentingnya menjaga keamanan data di era digitalisasi saat ini dapat dihubungkan langsung dengan ancaman yang dihadapi dari pihak-pihak yang tidak bertanggung jawab, seperti hacker. Serangan siber, seperti Man in The Middle (MiTM) dan Distributed Denial of Service (DDoS), menjadi potensi serius yang dapat mencuri informasi serta merusak kinerja server. MiTM, sebagai contoh, merupakan tindakan siber yang bertujuan untuk mencuri data dan memantau aktivitas korban dengan memanfaatkan koneksi internet yang tidak aman. Kantor Prolov, dalam menjalankan operasionalnya, turut menggunakan jaringan internet, namun demikian, hal ini membawa risiko serangan karena berbagi jaringan dengan pengguna internet umum ketika mengakses Virtual Private Server admin. Untuk mengatasi risiko tersebut, diperlukan implementasi Virtual Private Network (VPN) guna memperkuat keamanan data. Sebagai solusi, digunakanlah Zerotier sebagai platform yang terbuka dan terenkripsi. Penelitian ini bertujuan untuk merancang kebijakan keamanan yang jelas dan efektif. Setelah penerapan Zerotier, hasilnya menunjukkan bahwa Zerotier dapat berfungsi sebagai VPN yang bersifat open-source dan memiliki lapisan enkripsi. Penelitian ini berhasil meningkatkan efektivitas keamanan jaringan di Kantor Prolov, terutama karena Zerotier dapat dipantau dan dikontrol oleh administrator, memberikan kemudahan dalam manajemen jaringan.
ANALISIS TEKSTUR PADA CITRA IRIS MATA MENGGUNAKAN ALGORITMA GRAY LEVEL CO-OCCURENCY MATRIX Herliana, Asti; Arifin, Toni
Jurnal Pilar Nusa Mandiri Vol 15 No 2 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (917.978 KB) | DOI: 10.33480/pilar.v15i2.680

Abstract

According to data from the ministry of health, with the high intensity of use the gadget nowadays, therefore the number of people with eye disease is increasing. To overcome increase suffers of eye disease, it takes need early detection for who suffers potentially eye disease so that handling and prevention of blindness from eye disease effect can be immediately. The process detection of eye disease can be see in iris, there are several disease can be seen in iris among there are diabetic retinopathy and glaucoma. This research present texture analysis for iris images, the method is used GLCM (Gray Level Co-occurency Matrix) which is implemented using Matlab, and using 5 parameters namely contrast, correlation, energy, homogeneity and entropy. Process analysis texture is developed with preprocessing technique, the result of texture in images data iris can be recognized and produce the dataset of result from feature extraction with GLCM (Gray Level Co-occurency Matrix).
SISTEM MONITORING DAN KONTROL PEMBERIAN PAKAN IKAN BERBASIS IOT MENGGUNAKAN BLYNK Risman, Risman; Rachman, Rizal; Arifin, Toni
Jurnal Responsif : Riset Sains dan Informatika Vol 6 No 2 (2024): Jurnal Responsif : Riset Sains dan Informatika
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jti.v6i2.1627

Abstract

Dalam era modern, permintaan akan sistem otomatis pemberian pakan ikan berbasis IoT semakin tinggi. Penelitian ini bertujuan mengembangkan sistem monitoring dan kontrol pemberian pakan ikan berbasis NodeMCU ESP8266 dan aplikasi Blynk. Sistem ini menggunakan servo untuk pemberian pakan otomatis dan manual melalui Blynk. Uji coba menunjukkan performa baik, sistem responsif merespons perintah pengguna dari jarak jauh. Akurasi gerakan servo mencapai 100%, mengindikasikan kualitas sistem yang kuat. Penelitian menyimpulkan bahwa sistem ini dapat dioperasikan secara efisien oleh pengguna dari jarak jauh melalui Blynk. Sistem ini berpotensi mengelola pemberian pakan ikan secara optimal, mendukung pertumbuhan dan kesehatan ikan. Dalam perkembangan teknologi, sistem ini membuka peluang baru dalam menjaga keberlangsungan akuakultur dengan pendekatan yang lebih pintar dan terhubung secara digital.
Breast cancer identification using machine learning and hyperparameter optimization Arifin, Toni; Prasetyo Agung, Ignatius Wiseto; Junianto, Erfian; Rachman, Rizal; Wibowo, Ilham Rachmat; Agustin, Dari Dianata
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1620-1630

Abstract

Breast cancer identification can be analyzed through genomic analysis using gene expression data, one type of which is mRNA. This involves analyzing gene expression patterns of breast tissue samples to distinguish breast cancer from healthy tissue or to differentiate subtypes of different breast cancers. This research developed the right computational model for breast cancer classification using machine learning and hyperparameter optimization algorithms. The primary objective of this research is to utilize various machine learning algorithms to classify breast cancer based on gene expression and enhance the models developed in previous studies. This paper provides an extensive literature review of prior breast cancer classification research and offers new theoretical perspectives. This research used a problem-solving approach with conventional machine learning techniques, most notably the decision tree. It also evaluates other machine learning algorithms for comparison, including k-nearest neighbor, naïve bayes, random forest, extra tree classifier, and support vector machine. The evaluation process used classification reports that provide insight into the precision, recall, F1-score, and accuracy of each machine learning model. The evaluation results show that the performance of the decision tree algorithm model is superior and impressive, achieving 99.73% accuracy and a score of 1 for precision, recall, and F1-score.
Stock’s selection and trend prediction using technical analysis and artificial neural network Agung, Ignatius Wiseto Prasetyo; Arifin, Toni; Junianto, Erfian; Rabbani, Muhammad Ihsan; Mayangsari, Ariefa Diah
International Journal of Advances in Applied Sciences Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i1.pp151-163

Abstract

Stock trading offers potential profits when traders buy low and sell high. To maximize profits, accurate analysis is essential for selecting the right stocks, timing purchases, and selling at peak prices. The authors propose a new method for selecting potential stocks that are highly likely to rise in price. The method has two stages. First, technical analysis, using moving averages and stochastic oscillators, filters stocks with downward trends, anticipating a reversal and subsequent rise. Second, for selected stocks, future price trends are predicted using artificial neural networks, specifically long short-term memory (LSTM) with adaptive moment estimation (Adam) optimizer. The second step ensures that only stocks with increasing prices will be chosen for trading. This study analyzes five hundred Fortune 500 stocks over three different periods, with 250 days of data each. Simulations conducted in Python showed that technical analysis could filter 5 to 6 candidate stocks. Subsequently, the LSTM model predicted that only 4 of these stocks would have an upward trend. Validation shows that trend predictions are correct, resulting in an average profit of 5.51% within 10 working days. This profit outperforms the profits generated by other existing methods.
Co-Authors Ade Mubarok Adi Suparwo Adzkia, Sazila Azka Agustin, Dari Dianata Al Huda Ramadhan Ali Akbar Rismayadi Ani Solihat Anugrah, Gregian Bayu Asri Dwi Andini Asti Herliana, Asti Auliaddina, Siti Chusi Yanasari Daniel Ariesta Dwi Ariyandi Dwiza Riana Dwiza Riana Elin Sopiah Erfian Junianto Fahmi Permadi Achmad Fajar Nugraha Fakhri Habib Hawari Farizul Ma’arif Faslah Fadillah Fitriyani Fitriyani Fitriyani Fitriyani Fitriyani Fitriyani Gita Indah Hapsari Hendi Suhendi Hilda Farida Husniah Iedam Fardian Anshori, Iedam Fardian Ignatius Wiseto Prasetyo Agung Ihsan Mudzakir Imam Muhammad Mannar Shahih Insan Cahya Setia Maksum, Muhamad Shidiq Maya Nurjanah Mayangsari, Ariefa Diah Mayya Nurbayanti Shobary Muhamad Revin Reginal Harahap Muhamad Rifqi Alviandi Naufal Hidayah Surya Nur Huzumah Prasetya, Muhamad Azril Budi Prasetyo Agung, Ignatius Wiseto Rabbani, Muhammad Ihsan Rahayu Febryani Rahmaniza Maulana Rahmawati Rangga Sanjaya Rina Resmiati Risman Risman Rizal Rachman, Rizal Rizki Tri Prasetio Rohidin, Rifki Siti Syalwah Sri Mulyani Suhadi, Enda Syarif Hidayatulloh Taufik Hidayatulloh Tiara Nurwulandari Tiki Ramdhani Tyas Widyani Pratiwi Wibowo, Ilham Rachmat Wildan Wiguna Wira Sanjaya Yudi Ramdhani