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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Simetris Jurnal Buana Informatika Journal of Telematics and Informatics Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Scientific Journal of Informatics KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Format : Jurnal Imiah Teknik Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika JRSI (Jurnal Rekayasa Sistem dan Industri) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Informatika Universitas Pamulang Jurnal SITECH : Sistem Informasi dan Teknologi JATI (Jurnal Mahasiswa Teknik Informatika) Joined Journal (Journal of Informatics Education) Aiti: Jurnal Teknologi Informasi E-JOINT (Electronica and Electrical Journal Of Innovation Technology) Jurnal Pengabdian Masyarakat untuk Negeri (UN-PENMAS) Scientific Journal of Informatics Jurnal Pengabdian Siliwangi
Rianto, Rianto
Departement Of Informatics, Universitas Siliwangi
Agriculture, Biological Sciences & Forestry Humanities 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 Languange, Linguistic, Communication & Media Social Sciences Other

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Sistem pendukung Keputusan Penerimaan Karyawan Menggunakan Algoritma Multifactor Evaluation Process (Studi Kasus: XYZ Department Store Regional Priangan Timur) Lia Amelia
Joined Journal (Journal of Informatics Education) Vol 4 No 1 (2021): Volume 4 Nomor 1 (2021)
Publisher : Universitas Ivet

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31331/joined.v4i1.1681

Abstract

The management of human resources in a company affects many aspects that determine the success of the company. One of the efforts made is the selection of employees. Based on the interview results, XYZ Depstore in the process of recruiting employees is carried out regionally and is still carried out manually with the weight of the criteria that have not been detailed, this raises several problems including requiring a lot of time, the occurrence of human errors which cause unfair and inaccurate calculations. So that we need a system with a certain algorithm that automatically ranks prospective employees with accuracy, fairness and faster among the many applicants. In developing the system, the Multifactor Evaluation Process (MFEP) algorithm is used with three criteria and its weights that have been determined, namely 20% document, 10% psychological test and 70% interview. The system development method used is extreme programming. The system generates a sequence of recommendations for candidate employees who deserve to be accepted to help the company make decisions. Also equipped with features for prospective employees to submit application documents and personal data online, job vacancies information, selection announcements and report generation. Functional testing of the system is carried out using the black box method, showing the results of all the scenarios that have been made. The results of the calculation on the system are compared with the results of calculations on Microsoft Excel using 30 alternative prospective employees, showing the same calculation results, so that the accuracy rate is 100%.
Penerapan Metode Promethee Pada Aplikasi Perizinan Di Dinas Kominikasi Dan Informatika Kota Tasikmalaya Lucky Hermawan Roza
Joined Journal (Journal of Informatics Education) Vol 1 No 2 (2018): Volume 1 Nomor 2
Publisher : Universitas Ivet

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (473.131 KB) | DOI: 10.31331/joined.v1i2.716

Abstract

Dalam proses perkembangan teknologi informasi dan komunikasi, menjadi sangat penting bagi suatu organisasi perangkat daerah memiliki sistem yang dapat menunjang pemberian informasi kepada masyarakat dan menjadi salah satu tugas Dinas Komunikasi dan Informatika untuk menyeleksi sistem yang bisa dipublikasikan kepada masyarakat. Tujuan dari penelitian tugas akhir ini adalah membuat sistem perizinan pengembangan aplikasi dengan menggunakan metode PROMETHEE untuk menentukan ranking setiap permohonan yang masuk dan akan di proses oleh petugas Diskominfo dari ranking teratas. PROMETHEE adalah suatu metode penentuan urutan (prioritas) dalam analisis multi kriteria yang menawarkan kesederhanaan, kejelasan, dan kestabilan dalam proses analisisnya. Hasil dari penelitian ini yaitu dalam sistem layanan perizinan pengembangan aplikasi hasil perbandingan antara penentuan prioritas perizinan berdasarkan sistem menggunakan metode PROMETHEE dengan hasil penentuan berdasarkan perhitungan menggunakan Microsoft Excel terhadap 4 data uji maka diperoleh 4 data urutan pemohon pengembangan aplikasi dengan urutan yang sama. Dari hasil pengujian diatas dapat disimpulkan bahwa tidak ada perbedaaan yang signifikan antara hasil penentuan sistem menggunakan metode PROMETHEE dengan hasil penentuan menggunakan Microsoft Excel. Berdasarkan pengujian yang dilakukan ini diharapkan mampu membantu pihak Dinas Komunikasi dan Informatika dalam memutuskan prioritas pemohon yang harus di proses terlebih dahulu terutama dalam hal mengefisiensikan waktu dan untuk mengindari human error dalam penentuan prioritas perizinan pengembangan aplikasi.
Comparative Analysis Performance of K-Nearest Neighbor Algorithm and Adaptive Boosting on the Prediction of Non-Cash Food Aid Recipients Yustikasari, Yusi; Mubarok, Husni; Rianto, Rianto
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.32369

Abstract

Purpose: The implementation of this manual system is considered less accurate in obtaining the results of social assistance recipients. From these problems to overcome this problem, systematic calculations are needed. In processing data, a model is needed that can explain the data with its application, so a machine learning model is made that can help process the data.Methods: This study's classification of non-cash food social assistance receipts uses the K-Nearest Neighbor and Adaptive Boosting algorithms. This study will compare the performance of the two algorithms.Result: The results obtained for Adaptive Boosting are the best classification results with a maximum accuracy of 100% and produce a high AUC value of 1.0. In comparison, the ROC curve for the K-Nearest Neighbor algorithm produces an accuracy of 96% with an AUC value of 0.94.Novelty: ROC curves in the two algorithms are good classification results because the two graphs cross above the diagonal line and produce an AUC value included in the Excellent classification.
Sentiment Analysis Of Student Opinion Related To Online Learning Using Naïve Bayes Classifier Algorithm And SVM With Adaboost On Twitter Social Media Mohammad Rizal Ramli; Heni Sulastri; Rianto Rianto
Telematika Vol 20, No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.8827

Abstract

Twitter is one of the social media that functions to express opinions on issues or problems that are currently happening, such as problems in the social, economic, educational and other fields. One of the issues being discussed so far is online learning. The government has issued a policy, one of which is for all students to study at home online by using a network to be able to interact with each other like in the classroom. The government's reason for issuing this policy is to break the chain of the spread of the Covid-19 virus, which until now has not subsided. Regarding this online learning policy, there are pros and cons. This opinion is widely expressed on social media, one of which is Twitter. Sentiment analysis is a method for analyzing an opinion which aims to classify texts. The Naïve Bayes Classifier and Support Vector Machine methods are methods machine learning that can be used for sentiment analysis. The problem in classifying text is that the resulting accuracy is less than optimal, so feature selection or boosting is needed to improve its accuracy. In this study, optimization of boosting was carried out using Adaboost. The purpose of this study is to compare the performance of the algorithm before and after using Adaboost. The results of the sentiment analysis on online learning obtained the highest accuracy results by the Naïve Bayes Classifier algorithm coupled with Adaboost of 99.26%, with a precision of 99.39% and recall of 99.20%.
Particle Swarm Optimization dan Genetic Algorithm untuk analisis sentimen pemekaran Papua di Twitter berbasis Support Vector Machine Desi Purnamasari; Muhammad Adi Khairul Anshary; Rianto Rianto
AITI Vol 20 No 2 (2023)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v20i2.177-190

Abstract

Support Vector Machine (SVM) dapat digunakan untuk mengklasifikasikan analisis sentimen ke dalam sentimen positif atau negatif. Dalam penelitian ini data sentimen diambil dari Twitter dengan topik pemekaran Papua. Karena SVM memiliki kelemahan dalam pemilihan fitur pada saat pengklasifikasian maka diterapkan fitur optimasi algoritma SVM menggunakan feature selection. Dua metode feature selection yang digunakan adalah Particle Swarm Optimization (PSO) dan Genetic Algorithm (GA). Tweet yang diambil sebanyak 839 data tweet, yang kemudian dibagi menjadi 640 data untuk proses training dan 199 data untuk proses testing. Proses pengolahan data dibagi ke dalam dua tahap yakni data training dan data testing. Pengujian dilakukan sebanyak empat model yaitu dengan algoritma SVM, SVM+PSO, SVM+GA, SVM+PSO+GA. Hasil percobaan menunjukkan bahwa pemodelan SVM+PSO+GA memperoleh nilai akurasi terbaik sebesar 95.00% dengan nilai AUC sebesar 0.912 Kata Kunci : Analisis sentiment, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Support Vector Macine (SVM), Twitter.
PREDIKSI KEPUASAN PELANGGAN HOTEL: STUDI PERBANDINGAN ALGORITMA DECISION TREE DAN KNEAREST NEIGHBOR Dwi Ramti Asih; Rianto Rianto
Jurnal Ilmiah Informatika Komputer Vol 29, No 1 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i1.8324

Abstract

Customer satisfaction has become an important aspect for every business in today's competitive market. Understanding customer needs, wants, and expectations is critical for a business to provide outstanding customer service and retain customers. Therefore, this research represents a comparative study between two machine learning algorithms, Decision Tree and K-Nearest Neighbor, to predict hotel customer satisfaction. This study aims to identify which algorithm is more effective in predicting customer satisfaction by evaluating their performance using various metrics. The methodology used includes data preprocessing, feature selection, and machine learning model creation. The results show that the Decision Tree algorithm is superior to the K-Nearest Neighbor in terms of accuracy and precision. The findings from this study provide insights for businesses in the hospitality industry on how to predict customer satisfaction and improve their services.
Recognition of Organic Waste Objects Based on Vision Systems Using Attention Convolutional Neural Networks Models Aradea; Rianto; Husni Mubarok
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.6494

Abstract

Purpose: High population growth and increasing consumption patterns have resulted in significant organic waste production. The public often does not understand the correct way to deal with the problem of organic waste, including public awareness regarding the need for its management. Therefore, a system is needed to recognize waste objects based on various types. Currently, much research in this field has been studying object recognition, for example, the implementation of the Convolutional Neural Networks (CNN) model. However, there are still various challenges that must be addressed, including objects with diverse visual characteristics such as form, size, color, and physical condition. This research focuses on developing a system that enhances object recognition of waste, specifically organic waste, using an Attention Convolutional Neural Network (ACNN). By integrating attention mechanisms into the CNN model, this study addresses the challenges of recognizing waste objects with diverse visual characteristics. The proposed system seeks to improve the accuracy and efficiency of organic waste identification, which is crucial for advancing waste management practices and reducing environmental impact. Methods: This research combines a CNN architecture with an attention mechanism to create a better object detection environment called Attention-CNN (ACNN). The ACNN architecture employed consists of one layer input, three convoluted layers, three max-pooling layers, one attention layer, one flattened layer, four dropout layers, and two dense layers arranged in a certain way. Result: The research result shows that the model CNN with attention mechanism (ACNN) was slightly better at 86.93% than the standard model of CNN, which accounted for 86.70% in accuracy. Novelty: In general, the current use of CNN architecture to address waste object recognition problems typically employs standard architectures, resulting in lower accuracy for complex waste objects. In contrast, our research integrates attention mechanisms into the CNN architecture (ACNN), enhancing the model's ability to focus on relevant features of waste objects. This leads to improved recognition accuracy and robustness against visual variability. This distinction is important as it overcomes the limitations of standard CNN models in handling visually diverse and complex waste objects, thereby highlighting the novelty and contribution of our research.
LITERASI DIGITAL DAN MANAJEMEN USAHA BAGI PEDAGANG KECIL DAN SEKTOR USAHA INFORMAL Rianto Rianto; Husni Mubarok; Aradea Aradea; Nur Widiyasono
Jurnal Pengabdian Siliwangi Vol 9, No 1 (2023)
Publisher : LPPM Univeristas Siliwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jsppm.v9i1.6154

Abstract

Pedagang kecil atau pelaku usaha informal (pedagang warung, pedagang keliling, penjual ikan, pedagang gorengan,dan lain-lain) merupakan jenis usaha perorangan atau kelompok yang dilakukan oleh sebagian besar masyarakat Indonesia. Pelaku usaha tersebut menjajakan berbagai produk-produk yang diproduksi oleh sendiri ataupun produk yang dibuat oleh pelaku usaha lain. Produk-produk yang dijual diantaranya aneka lauk pauk, kue basah, aneka gorengan, es kelapa muda, produk pertanian, berbagai macam ikan dan produk lainnya. Hasil dari survei awal masih terdapat kekurangan terkait kurang nya pemahaman para pelaku usaha tersebut dalam manajemen usaha baik dalam pemasaran dan manajemen keuangan terutama dalam memanfaatkan teknologi informasi. Kelebihan yang di peroleh pelaku usaha dalam memanfaatkan teknologi informasi diharapkan dapat meningkatkan kapasitas penjualan produk serta pengelolaan keuangannya. Fokus utama dari program ini adalah untuk memberikan pemahaman dan gambaran mengenai pemanfaatan teknologi informasi baik penggunaan perangkat, aplikasi keuangan dan  media sosial dalam upaya meningkatkan hasil penjualan dan manajemen usahanya. Selain itu, diberikan gambaran proses bisnis yang harus dilakukan oleh mitra agar program ini dapat berjalan secara berkelanjutan. Program ini memiliki tahapan pelaksanaan yaitu perencanaan, pelaksanaan, serta monitoring dan evaluasi hasil pelaksanaan program. Program pengabdian ini telah diselenggarakan dengan melakukan kegiatan sosialiasi dan berbagi pengetahuan proses manajemen usaha dan marketing dengan bantuan aplikasi digital, media sosial dan media digital lainnya pada kedua mitra. Tujuan dari program pengabdian ini diharapkan dapat membantu berupa peningkatan manajemen pengetahuan mitra, penerapan IT dan peningkatan hasil hasil usaha mitra. Manajemen usaha dan pemasaran tersebut dapat memberikan manfaat pada mitra dan masyarakat sekitar, terutama terkait model usaha mitra yang lebih baik.
ITGbM PELATIHAN DAN PENERAPAN FINGER PRINT TIME ATTENDANCE UNTUK PENCATATAN DATA KEHADIRAN PERANGKAT DESA Rianto Rianto; Rohmat Gunawan
Jurnal Pengabdian Siliwangi Vol 3, No 2 (2017)
Publisher : LPPM Univeristas Siliwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jsppm.v3i2.354

Abstract

Proses pencatatan data kehadiran perangkat desa perlu dikelola dengan baik, karena mempengaruhi kinerja pelayanan kepada masyarakat. Pencatatan data kehadiran secara konvensional, biasanya dilakukan saat jam masuk kerja dengan membubuhkan tanda tangan pada sebuah buku atau form daftar hadir. Proses pencatatan seperti ini sangat sederhana dan mudah diimplementasikan, tetapi memiliki kekurangan, diantaranya mudah dimanipulasi dengan cara menitipkan tanda tangan kepada orang lain. Beberapa metode untuk menyelesaikan masalah pada pencatatan data kehadiran konvensional, diantaranya dengan menggunakan alat bantu perangkat elektronik berupa : time recording machine, id card with barcode system, magnetic card dan RFID card. Walaupun sudah menggunakan alat bantu elektronik, tetapi masih terdapat kurangan, karena kartu identitas dapat dititipkan kepada orang lain. Dalam kegiatan pengabdian ini diusulkan untuk menerapkan sistem pencatatan kehadiran menggunakan alat bantu pencatat kehadiran berbasis sidik jari (finger print time attendance). Hasil dari kegiatan PPM ini, telah diterapkannya sistem pencatatan data kehadiran berbasis sidik jari di lokasi mitra pengabdian. Pola sidik jari menjadi alat untuk validasi kehadiran setiap perangkat desa. Laporan data kehadiran secara otomatis diproses oleh sistem, dan dapat ditampilkan atau dicetak ketika dibutuhkan.
Sosialisasi dan Pelatihan Penggunaan Aplikasi SMART Guna Mendukung Tertib Administrasi di Lingkungan Rukun Tetangga Rahmatulloh, Alam; Rianto, Rianto; Gunawan, Rohmat; Rizal, Randi
UN PENMAS (Jurnal Pengabdian Masyarakat untuk Negeri) Vol 4 No 1 (2024): UN PENMAS Vol 4 No 1
Publisher : LPPM Universitas Narotama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29138/un-penmas.v4i1.2562

Abstract

Rukun Tetangga (RT) is a community organization formed through deliberation and consensus of the local community to maintain harmony in life. Managing population administration at the RT level is one of the responsibilities of the RT Head. Population administration needs to be managed well, because population data is a basic statistical source in making various policies. In order to create administrative order in managing population data, in this service activity, socialization and training on the use of the SMART application (Sistem Informasi Manajemen Administrasi Rukun Tetangga) is carried out. Residents of RT 04 RW 07 Arjasari Village, Leuwisari District, Tasikmalaya Regency are partners in this service activity. There are several stages carried out in this service activity, including: stage 1 initial preparation, stage 2 main program, stage 3 closing the activity. Socialization and training on using the SMART application was carried out at partner locations on Friday 15 September. This application is expected to facilitate and make it easier for RT Heads and residents to complete population data and support orderly administration.
Co-Authors Acep Irham Gufroni Alam Rahmatulloh Algi Muhkamaat Abdillah Aradea Aradea Ari Sandriana Baetulloh, Uci Desi Purnamasari Dwi Ramti Asih Fauzi Athallah, Zico Firmansah, Teguh Anugrah Firmansyah Maulana Fransiska, Susanti Galih Permana Givany Salsabila Siti Rukoyah Gunawan, Rohmat Gunawan, Rohmat Hadi Purwadi Heni Sulastri Hilmi Aziz Husni Mubarok Husni Mubarok Husni Mubarok Hutari Maulida Kurnia Iqbal Muhammad Fajar Nuralam Kurnia, Hutari Maulida Lusi Robiatul Fadilah Meidiyan, Mohamad Gibran Mohammad Rizal Ramli Muhammad Adi Khairul A Muhammad Adi Khairul Anshary Neng Ika Kurniati Nur Widiyasono Oktalia, Silvia Dwi R. Moch. Dysa Satria Anggara Rahmi Nur Shofa Rizal, Randi Ruswati, Ruswati Shihab, Muhammad Quraish Shofa, Rahmi Nur Shofa, Rahmi Nur Silvia Dwi Oktalia Siti Rukoyah, Givany Salsabila Yustikasari, Yusi