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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) 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) 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
Yudi Ramdhani
Universitas Adhirajasa Reswara Sanjaya
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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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.
Searching Sahih Hadiths Based on Queries using Neural Models and FastText Susanti, Sari; Najiyah, Ina; Ramdhani, Yudi; Herliana, Asti; Muckti, Masaldi Kharisma; Oktaviani, Fani Rahma
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.467

Abstract

Hadith is the second source of Islamic law after the Qur’an, and the availability of accurate and easily accessible information about hadith is crucial, as it directly affects a person’s belief (aqidah). This highlights the importance of having hadith collections as essential guidance in everyday life. Today, digital versions of hadiths are available in various applications, e-books, and websites. However, users often complain that these sources are incomplete and do not contain the entire collection of the Prophet's hadiths from al-Kutub as-Sittah. Additionally, the complex presentation of these digital resources makes it difficult to find relevant hadiths efficiently. This study aims to improve access to accurate and relevant hadith information, focusing specifically on al-Kutub as-Sittah, using Information Retrieval systems that search for hadiths based on keywords. IR is employed because it has proven effective in retrieving precise documents according to the search terms. A Neural Network is used to match user queries with the document collection, while FastText word embedding is implemented for text representation. FastText is particularly useful for detecting similar meanings across different words, which is essential when interpreting Indonesian-translated hadiths that require nuanced understanding. The dataset used in this study consists of 31,275 Indonesian-translated hadiths from al-Kutub as-Sittah. In this study, it was found that many hadith translations have ancient language so that query reformulation is needed to get the right hadith because users often enter commands with currently trending words. In this study, it was also found that word2vec has less performance than FastText in weighting words in hadith translations. The results indicate that the neural network performs well in retrieving relevant hadith content according to the user’s commands or keywords. With a training data proportion of 70% and a testing data proportion of 30%, the Recall value was 0.7721 and the Precision value was 0.75112.
Implementasi Metode Simple Additive Weighting (SAW) Dalam Penilaian Kinerja Karyawan Pada LPK Pelita Massa Berbasis Web Rizal Rosidin; Yudi Ramdhani
ULIL ALBAB : Jurnal Ilmiah Multidisiplin Vol. 1 No. 5: April 2022
Publisher : CV. Ulil Albab Corp

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

Abstract

Although employee performance assessments at LPK Pelita Massa have not yet been implemented, the company itself has plans to implement employee performance appraisals. Employee performance appraisals can motivate employees and increase employee loyalty to the company. However, the LPK Pelita Massa still finds it difficult to decide on the best employees according to the criteria. A technique to overcome a problem in terms of systematic and unsystematic is also called a decision support system (DSS). The Simple Additive Weighting (SAW) method will be implemented into a decision support system in the hope of being a solution to the problems experienced by LPK Pelita Massa in determining the best employees. The results of interviews and observations at LPK Pelita Massa became the primary data for this study in determining the criteria and alternatives, and five (5) criteria were obtained, namely responsibility, work knowledge, cooperation, work quality, and attitude, after knowing the criteria, then doing calculations, and ranking alternatives. A Web-based decision support system is the result of this research
STUDI KOMPARATIF ALGORITMA MACHINE LEARNING PADA ANALISIS SENTIMEN MEDIA SOSIAL Panjaitan, Febriyanti; Ce, Win; Oktafiandy, Hery; Kanugrahan, Ghanim; Ramdhani, Yudi; Hafizh Cahaya Putra, Vito; Permai, Antika
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 2 (2025): JATI Vol. 9 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i2.13277

Abstract

Analisis sentimen di Twitter telah menjadi salah satu topik utama dalam penelitian terkait opini publik di bidang ekonomi, politik, dan isu sosial. Penggunaan machine learning dalam analisis sentimen memungkinkan untuk memproses data teks secara efisien. Penelitian ini bertujuan untuk mengeksplorasi literatur terkait analisis sentimen menggunakan metode machine learning pada Twitter dalam konteks ekonomi, politik, dan isu sosial. Metode yang digunakan adalah Systematic Literature Review (SLR), dengan pengumpulan artikel dari tiga database utama: IEEE Xplore, Google Scholar, dan Scopus. Setelah menerapkan kriteria inklusi dan eksklusi, 45 artikel relevan terpilih untuk dianalisis. Hasil penelitian menunjukkan bahwa Support Vector Machine (SVM) memiliki performa terbaik dengan akurasi rata-rata 85.3%, diikuti oleh Random Forest (83.7%) dan Naïve Bayes (81.5%). KNN dan Decision Tree menunjukkan performa lebih rendah, kemungkinan karena sensitivitas terhadap data yang tidak seimbang. Tren penelitian mengindikasikan bahwa analisis sentimen di bidang ekonomi lebih banyak berkaitan dengan dampak kebijakan ekonomi, di bidang politik fokus pada opini publik terkait pemilu dan kebijakan pemerintah, sementara di bidang isu sosial berkaitan dengan gerakan sosial dan kebijakan kesehatan.
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. Ce, Win Cucu Ika Agustyaningrum Djaya Siswaja, Hendy Doni Purnama Alamsyah Dwiniati, Dwiniati Dwiza Riana Dwiza Riana Dwiza Riana Erfian Junianto Fadila Andini Febriyanti Panjaitan Fitri Khoirunnisa Fitriyani Fitriyani 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 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 Oktafiandy, Hery Oktaviani, Fani Rahma Permai, Antika Rangga Sanjaya Rein Lantin Resdiana Pratama Riski Mardhianto Rizal Rosidin Rizki Tri Prasetio 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