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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Infotech Journal Sistemasi: Jurnal Sistem Informasi Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Journal of Information Technology and Computer Science (JOINTECS) Produktif : Jurnal Ilmiah Pendidikan Teknologi Informasi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Tekno Kompak Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) INFORMASI (Jurnal Informatika dan Sistem Informasi) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika C.I.T. Medicom Jurnal Infortech JAMI: Jurnal Ahli Muda Indonesia Journal of Applied Data Sciences Jurnal Responsif : Riset Sains dan Informatika DEVICE Jurnal Dialektika Informatika (Detika) Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika Jurnal Sosial dan Abdimas JURNAL ILMU SOSIAL DAN ILMU POLITIK, STISIPOL RAJA HAJI Kohesi: Jurnal Sains dan Teknologi Ulil Albab Jurnal Infortech
Yudi Ramdhani
Satu University
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology 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 Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Neuroscience Social Sciences Transportation Other

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Sentiment Analysis Tanggapan Masyarakat Tentang Hacker Bjorka Menggunakan Metode SVM Taufik Agung Pramana; Yudi Ramdhani
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 6, No 1 (2023): Februari 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.v6i1.5583

Abstract

Abstrak— Pada pertengahan tahun 2022, Indonesia dibuat gempar oleh kehadiran hacker dengan nama Bjorka, pasalnya Bjorka berhasil meretas situs Kementrian Komunikasi dan Informatika (KEMKOMINFO). Bjorka tidak melakukan aksinya satu atau dua kali, namun sering kali Bjorka membuat gempar seluruh masyarakat Indoneisa. Bagaimana tidak, selain meretas situs Kementrian Komunikasi dan Informatika, Bjorka juga berhasil mencuri dokumen rahasia milik Badan Intelejen Negara (BIN). Selain berhasil mencuri, Bjorka juga tidak ragu untuk menyebarluaskan dokumen rahasia tersebut dalam berupa kalimat di sosial media Twitter. Salah satu isu dokumen rahasia milik Indonesia yang Bjorka bocorkan adalah mengenai kasus pembunuhan Munir. Sebagai negara yang memiliki 19,5 juta pengguna aktif Twitter, tentunya hashtag Bjorka sering kali menduduki peringkat teratas, ini berarti banyak dari masyarakat di Twitter yang membicarakan Bjorka dalam cuitan Twitternya. Pada cuitan Twitter tersebut kita bisa menemukan banyak komentar positif dan komentar negatif. Penulis menggunakan metode Support Vector Machine untuk mengetahui apakah tanggapan masyarakat di Twitter termasuk positif atau negatif. Berdasarkan hasil analisis, akurasi terbaik didapatkan yaitu metode SVM dalam sentiment analysis tanggapan masyarakat Indonesia tentang Hacker Bjorka dibandingkan metode NBC.Kata Kunci: Analisis sentimen, SVM, Bjorka Abstract— In mid-2022, Indonesia was shocked by the presence of a hacker named Bjorka, because Bjorka managed to hack the Ministry of Communication and Information (KEMKOMINFO) website. Bjorka didn't do it once or twice, but Bjorka often caused an uproar among Indonesian people. How could it not be, besides hacking the Ministry of Communication and Informatics website, Bjorka also managed to steal confidential documents belonging to the State Intelligence Agency (BIN). Apart from being successful in stealing, Bjorka also did not hesitate to disseminate these secret documents in the form of sentences on social media Twitter. One of the issues of secret documents belonging to Indonesia that Bjorka leaked was regarding the Munir murder case. As a country with 19.5 million active Twitter users, of course, the hashtag Bjorka often tops the rankings. This means that many people on Twitter talk about Bjorka in their Twitter tweets. On the Twitter tweet, we can find many positive comments and negative comments. The author uses the Support Vector Machine method to find out whether people's responses on Twitter are positive or negative. Based on the results of the analysis, the best accuracy was obtained, namely the SVM method in sentiment analysis of Indonesian people's responses to the Bjorka Hacker compared to the NBC method..Keyword : Sentiment Analysis, SVM, Bjorka
Application of bidirectional gated recurrent unit algorithm for rainfall prediction Pratama Syahdan Nabil; Yudi Ramdhani
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 4 (2023): September : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.522.pp188-198

Abstract

The management of water resources and various industrial sectors is highly dependent on rainfall. To avoid negative impacts such as floods, droughts and other natural disasters, rainfall forecasts must be accurate and timely.This research aims to find the best algorithm for predicting rainfall. In this study, modeling was carried out using the Bandung city rainfall dataset from 2018 to 2022 using the Bidirectional Gated Recurrent Unit (BiGRU) method. Bidirectional Long Short Term Memory (BiLSTM), Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM) are used to compare the performance of the BiGRU algorithm. The test findings show that, with value Root Mean Squared Error (RMSE) and R2 Score BiGRU gives the best results with the lowest error rate. The algorithm with the biggest error rate is LSTM. This study advances strategies for predicting rainfall that can be applied to managing water resources and responding to natural disasters related to rainfall.
PENGGUNAAN OTIMASI ATRIBUT DALAM PENINGKATAN AKURASI PREDIKSI DEEP LEARNING PADA BIKE SHARING DEMAND Hidayatulloh, Syarif; Mustajab, Muhammad Amar; Ramdhani, Yudi
INFOTECH journal Vol. 9 No. 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v9i1.4530

Abstract

Bersepeda kembali populer pasca pandemi Covid 19 yang terjadi di Indonesia kemarin. Dalam studi ini, algoritma yang paling umum digunakan diuji, termasuk Neural Nets, Generelized Linear Models, Support Vector Machines, Random Forests, dan Deep Learning. Penelitian dilakukan dalam lima model algoritma prediktif dengan sepuluh percobaan menggunakan validasi silang, dan dipilih nilai akurasi terbaik. Berdasarkan perbandingan algoritma tersebut, algoritma Deep Learning memiliki nilai Accuracy sebesar 90% dan AUC sebesar 0,770. Validasi Croos dengan X fold adalah dasar dari algoritma perbandingan ini. Algoritma pembelajaran mendalam ditemukan memiliki nilai akurasi 90%, yang 4-5% lebih rendah dari empat algoritma lainnya. Dengan peningkatan yang signifikan pada penelitian tersebut, maka nilai akurasi untuk optimasi bobot menggunakan algoritma Forward optimize adalah sebesar 95,63%. Berdasarkan hasil percobaan, peneliti menyimpulkan bahwa percobaan tersebut berhasil meningkatkan nilai akurasi dari algoritma deep learning.
ANALISIS ALGORITMA FP-GROWTH DAN APRIORI UNTUK MENEMUKAN MODEL ASOSIASI TERBAIK PADA DATASET ONLINE RETAIL Meirynda Lastika Rahimsyah; Yudi Ramdhani
Kohesi: Jurnal Sains dan Teknologi Vol. 3 No. 1 (2024): Kohesi: Jurnal Sains dan Teknologi
Publisher : CV SWA Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3785/kohesi.v3i1.2866

Abstract

In the digital era, the online retail industry is growing rapidly and is becoming an important sector. However, challenges arise in the analysis of sales transaction data on the Online Retail dataset. This study aims to overcome problems in the analysis of sales transaction data in the Online Retail dataset. The main focus includes selecting the optimal association algorithm between FP-Growth and Apriori, identifying relevant association models on complex datasets, and the efficiency and performance of algorithms in processing sales transaction data. The method used is association data processing using the FP-Growth and Apriori algorithms. Implementation of the association rule involves adding a lift metric as a measure of association strength. Measurement of processing time is also carried out to determine the efficiency of implementation. The results showed that FP-Growth and Apriori could produce an association model with the same frequent itemset and value matrix, namely a support value of 0.12 and a confidence value of 0.96, but there were differences in the resulting model order. The Apriori algorithm produces a model with the highest support value at index 18, while FP-Growth at index 10. In addition, the FP-Growth algorithm shows an advantage in faster processing time (0.004 seconds) compared to Apriori (0.007 seconds). This research provides a better understanding of the use of association algorithms in the context of the online retail industry.
Perancangan Sistem IoT Smart Fisher Untuk Kelompok Budidaya Ikan Kaliwungu Rahayu Ramdhani, Yudi; Hariyanti, Ifani; Sandini, Dwi; Susanti, Sari; Najiyah, Ina
Jurnal Sosial & Abdimas Vol 5 No 1 (2023): Jurnal Sosial & Abdimas
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jsa.v5i1.1071

Abstract

Kelompok masyarakat yang memiliki mata pencaharian melalui budidaya ikan salah satunya adalah Kelompok Budidaya Ikan Kaliwungu Rahayu yang berlokasi di Dusun Panglajan Desa Cintaratu Kec Parigi Kabupaten Pangandaran. Berdasarkan hasil temuan yang didapatkan dari pembudidaya ikan, baik yang berfokus pada pembenihan maupun pembesaran sama-sama merasakan masa panen yang lama. Pada pembudidaya yang berfokus pada pembenihan masa panen kurang lebih dicapai selama 3 bulan, sedangkan untuk yang berfokus pada pembesaran dicapai selama 4 bulan. Masa panen yang kurang optimal dipengaruhi tidak adanya alat penunjang. Peralatan ini yang berpengaruh pada manajemen kualitas air, selama ini manajemen kualitas air dilakukan secara tradisional dan berdasarkan pengalaman yang diperoleh. Faktor tersebut juga menyebabkan bidang budidaya ikan dipandang memiliki nilai ekonomi yang rendah. Kegiatan pengabdian masyarakat ini bertujuan untuk membantu Kelompok Budidaya Ikan Kaliwungu Rahayu untuk menyelesaikan masalahnya. Metode yang yang digunakan dalam pengabdian masyarakat ini terdiri dari tahapan observasi, wawancara dengan mitra, survey lokasi kolam tempat budidaya ikan, serta sosialisasi hasil perancangan model teknologi yang akan dibuat. Hasil pengabdian masyarakat ini sebuah model perancangan sistem IoT yang akan diterapkan untuk manajemen kolam, pada Kelompok Budidaya Ikan Kaliwungu Rahayu.
Deep neural networks and conventional machine learning classifiers to analyze thoracic survival data Ika Agustyaningrum, Cucu; Ramdhani, Yudi; Purnama Alamsyah, Doni; B. Hariyanto, Oda I.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3686-3694

Abstract

Lung cancer is a prevalent global health concern and most prevalent malignancy in Indonesian hospitals. Following thoracic surgery, patients were categorized into two classes: individuals who experienced mortality within a year and those who achieved survival. Despite being about socks, the dataset for the deceased category consisted of 70 data samples, while the dataset for the final group comprised 400 samples. Data calculation involves the utilization of both deep neural networks and standard machine learning algorithms. The study use the Python programming language to evaluate the algorithms, and it measures their performance using metrics such as accuracy, F1-Score, precision, recall, receiver operating characteristic (ROC), and area under curve (AUC). The test results indicate that the deep neural network method achieves an accuracy of 95,56%, an F1 score of 79,24%, a precision of 91,96%, a recall of 85,52%, and an AUC of 85,52%. This study suggests that utilizing deep neural network data mining techniques, specifically with a cross-validation fold of 10, variations of six hidden layer encoder-decoder, relu, sigmoid activation function, optimizer Adam, and learning rate of 0,01, dropout rate of 0,2. Employing the Synthetic Minority Over-sampling Technique data preprocessing method, can effectively analyze thoracic patient survival data sets.
Strategi Dinas Kebudayaan dan Pariwisata Kota Tanjungpinang dalam Pemulihan Sektor Pariwisata pada Situasi Pandemi Covid -19 Indriyati, Susana; Nadia, Putri; Siti Utari, Diah; Dwiniati, Dwiniati; Ramdhani, Yudi
Jurnal Ilmu Sosial dan Ilmu Politik Vol. 4 No. 2 (2023): Jurnal Ilmu Sosial dan Ilmu Politik
Publisher : STISIPOL Raja Haji

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56552/jisipol.v4i2.107

Abstract

No foreign tourists visited Tanjung Pinang in July 2020 as international passenger ships have been suspended since April 2020 due to the Covid-19 pandemic. In July 2020, there were 1,765 tourists visiting the Riau Islands, 1,754 (99.38%) from the entrance of Batam City and 11 (0.62%) from the entrance of Bintan Province. On the contrary, in July 2020, no foreign tourists visited Tanjung Pinang City. Since no tourists visited Tanjung Pinang City in July 2020, the number of tourists visiting Tanjung Pinang City, decreased by around 81.71 percent compared to the same period last year. The purpose of this study was to determine the Strategy of the Tanjungpinang City Culture and Tourism Office in the recovery of the tourism sector in the covid-19 pandemic situation. The result of this study is that the Tanjungpinang City Culture and Tourism Office on the recovery of the tourism sector in the covid-19 pandemic situation has not been optimal because it sees weaknesses both internally & externally, this is found in the following, namely the tourism office is still carrying out tactics before covid, even after covid this has not been formulated specific strategies. Tourism development is needed, development of tourism destinations & development of human resources (HR). The Tanjungpinang City Tourism Office has collaborated using particulate parties to increase the tourism potential of the region tourist visits to the Tanjungpinang City area.
PENDEKATAN ALGORITMA NEURAL NETWORK DAN GENETIC ALGORITHM UNTUK PREDIKSI PENYAKIT GINJAL KRONIS Siswaja, Hendy D; Ramdhani, Yudi
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.v6i2.1778

Abstract

Penyakit ginjal kronis (PGK) merupakan masalah kesehatan masyarakat global yang mempengaruhi sekitar 10% dari populasi dunia. Persentase prevalensi PGK di China adalah 10,8%, dan rentang prevalensinya adalah 10%-15% di Amerika Serikat. Seiring dengan perkembangan Artificial Intelligence (AI) dimana Machine Learning (ML) merupakan subbagian dari AI, penelitian ini mencoba memanfaatkan algoritma Neural Network, optimasi data berbasis Genetic Algorithm, dan k-fold Cross Validation dengan nilai k berkelipatan 10, yaitu 10, 20, 30, 40, dan 50 untuk memprediksi apakah seorang pasien mengidap PGK atau tidak dari dataset yang berisi hasil uji klinis pasien tersebut. Hasil penelitian ini mengungkapkan bahwa algoritma Neural Network dengan optimasi data berbasis GA mampu memperoleh tingkat akurasi sampai dengan 98,75% dan nilai AUC sebesar 0,999 sehingga dapat disimpulkan bahwa algoritma Neural Network dengan optimasi berbasis GA ini dapat dikembangkan lebih lanjut menjadi sebuah aplikasi ataupun bagian dari sistem kesehatan sehingga tingkat diagnosa pasien yang mengidap PGK dapat lebih cepat dilakukan dengan tingkat akurasi yang tinggi dan dapat meningkatkan peluang kesembuhan bagi pasien tersebut.
Exploring ADR Trends: A Data Mining Approach to Hotel Room Pricing, Cancellations, and EDA Hikmawati, Nina Kurnia; Ramdhani, Yudi; Wartika, Wartika
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.165

Abstract

This study investigates the intricacies of hotel reservation cancellations by analyzing a comprehensive dataset that includes information from both City Hotel and Resort Hotel. Through a thorough examination of various aspects, the research provides detailed insights into cancellation tendencies, daily rates, seasonal trends, and the influence of geographic factors and market segments on cancellation behavior. The overall cancellation and non-cancellation ratios indicate a notable non-cancellation rate of 62.86%, showcasing a high level of guest confidence in their reservations. Conversely, the 37.14% cancellation ratio raises concerns about potential negative repercussions. A comparative analysis between City Hotel and Resort Hotel reveals a significant difference in cancellation rates, emphasizing the need for tailored strategies at City Hotel to enhance booking stability. The study on Average Daily Rate (ADR) for both hotels bring attention to price differences and seasonal trends. Resort Hotel's higher ADR suggests potential advantages in location or amenities. Seasonal trends, particularly the highest ADR during the summer, provide valuable insights for resource planning. The variation in cancellation rates based on countries emphasizes the importance of focused strategies in regions with high cancellation rates, as seen with Portugal having the highest cancellation rate (77.70%). Analysis of hotel customer market segments identifies Online Travel Agencies (OTA) as the segment with the highest cancellation rate (46.97%). These findings present opportunities for tailored marketing and cancellation policies based on the characteristics of each segment. In conclusion, this research offers strategic insights for hotel managers to enhance booking stability, design competitive pricing policies, and understand the impact of geographic factors and market segments on cancellation behavior.
Analisis Sentimen Aplikasi Gojek Menggunakan SVM, Random Forest dan Decision Tree Kanugrahan, Ghanim; Putra, Vito Hafizh Cahaya; Ramdhani, Yudi
Jurnal Infortech Vol 6, No 2 (2024): Desember 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v6i2.24594

Abstract

Semakin banyak orang di dunia menggunakan aplikasi seluler di smartphone yang mereka miliki lebih dari sekadar alat hiburan, tetapi juga untuk memenuhi kebutuhan sehari-hari. Hal ini telah menyebabkan munculnya aplikasi seperti Gojek, sebuah perusahaan Super-app yang menyediakan solusi transportasi dan keperluan lainnya. Namun, Gojek menghadapi persaingan dari aplikasi serupa. Dengan kompetisi yang intens, memastikan kepuasan pengguna sangat penting untuk kesuksesan aplikasi Gojek. Review di platform seperti Google Play Store memberikan data berharga bagi pengembang untuk meningkatkan kualitas aplikasi dan pengalaman pengguna melalui pembaruan yang berkelanjutan. Makalah ini menganalisis kepuasan pelanggan aplikasi Gojek menggunakan pembelajaran mesin pada review pengguna dari Google Play Store yang diperoleh dari repositori data Kaggle. Dari 224.044 review awal, dataset dikurangi menjadi 65.584 review. Analisis mengungkapkan sentimen yang bervariasi, dengan kepuasan tinggi pada review bintang 5 dan keluhan umum tentang layanan yang lambat pada penilaian yang lebih rendah. Sembilan variasi model pembelajaran mesin, termasuk SVM, Random Forest, dan Decision Tree, digunakan untuk mengevaluasi data yang diterima. Algoritma SVM diidentifikasi sebagai yang paling efektif untuk klasifikasi sentimen. Hasil ini menunjukkan bahwa algoritma SVM adalah algoritma terbaik untuk digunakan dalam menganalisis review Gojek.
Co-Authors Achmad Nizar Hidayanto Ade Mubarok Adi Nurseptaji Adi Nurseptaji Adi Nurseptaji Ali Akbar Rismayadi Ali Akbar Rismayadi Alpiansah, Agung Bia Amin Fahri Andre Prayoga Arey Arey Asti Herliana, Asti B. Hariyanto, Oda I. Cakra Mahendra Putra Ce, Win Cucu Ika Agustyaningrum Dhia Fauziah Apra Djaya Siswaja, Hendy Doni Purnama Alamsyah Doni Purnama Alamsyah Dwiniati, Dwiniati Dwiza Riana Dwiza Riana Dwiza Riana Erfian Junianto Fadila Andini Febriyanti Panjaitan Febriyanti Panjaitan Fitri Khoirunnisa Fitriyani Fitriyani Ghanim Kanugrahan Haerul Hafizh Cahaya Putra, Vito Hariyanti, Ifani Hery Oktafiandi Hikmawati, Nina Kurnia Hiya Nalatissifa Hizaz Zakaria Yahya Iedam Fardian Anshori, Iedam Fardian Ina Najiyah Indriyati, Susana Irgi Mahendrata Saputra Kanugrahan, Ghanim Marko, Niki Mayya Nurbayanti Shobary Meirynda Lastika Rahimsyah Miftah Farid Adiwisastra Miftahul Rizal Moch Iqbal Tawakal Muckti, Masaldi Kharisma Muhamad Zakhy Syahaf Muhammad Amar Mustajab Mustajab, Muhammad Amar Nadia, Putri Nadiyah Hidayati Nanda Dwi Husna Sadikin Nandi Dwi Husni Sadikin Niki Marko Novia Andini Oktafiandy, Hery Oktaviani, Fani Rahma Permai, Antika Pratama Syahdan Nabil Pratiwi Pratiwi Rangga Sanjaya Rein Lantin Resdiana Pratama Riski Mardhianto Rizal Rosidin Rizki Tri Prasetio, Rizki Tri Sadikin, Nanda Dwi Husna Sadikin, Nandi Dwi Husni Salman Topiq Salsabila Ayuni Kaffah Sandini, Dwi Saputra, Irgi Mahendrata Sari Susanti Sari Susanti, Sari Satrio Rully Priyambodo Siti Rendani Anjaryanti Siti Utari, Diah Suherman, Himam Dwipratama Syarif Hidayatulloh Syarif Hidayatulloh Syarif Hidayatulloh Taufik Agung Pramana Tessa Putri Mallini Toni Arifin Wartika, Wartika Win Ce