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All Journal Jurnal Algoritma Jurnal Wawasan Ilmiah Jurnal Teknologi Informasi dan Ilmu Komputer JAIS (Journal of Applied Intelligent System) Jurnal Ilmu Sosial dan Ilmu Politik JOIN (Jurnal Online Informatika) Edu Komputika Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Seminar Nasional Teknologi Informasi Komunikasi dan Industri JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Elektronika dan Telekomunikasi Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal ULTIMATICS Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURTEKSI Jurnal Sistem Cerdas Aiti: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Teknika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal PkM MIFTEK Jurnal Algoritma Engineering Science Letter IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Integrated : Journal Of Information Technology and Vocational Education JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Journal of Intelligent Systems Technology and Informatics International Journal of Applied Information Systems and Informatics
Dede Kurniadi
Institut Teknologi Garut
Humanities Automotive Engineering 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 Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Social Sciences Other

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ASSET MANAGEMENT SYSTEM DESIGN OF VILLAGE BASED ON GEOGRAPHIC INFORMATION SYSTEM Heri Suhendar; Joko Iskandar; Dede Kurniadi; Yosep Septiana
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.4.299

Abstract

Management of an asset by the government is a process that starts from planning to asset inventorying that have been pre-existing or obtained from legitimate assistance so that they can managed appropriately and beneficially for the community. For the government, especially in village regions, management of assets is very important, so that both government apparatus and village community get complete, accurate and real-time information about the assets owned by the village government so that the information can be used for activities of village government and communities optimally. The goal of this research is to design and build an asset management system based on geographic information system (GIS) for government in the village. The GIS-based asset management design system uses a waterfall-model approach with five stages, namely: 1) Analysis, 2) Design, 3) Implementation, 4) Integration Testing, and 5) Maintenance. This asset management application is built with web-based technology using the Leaflet framework that supports Web Map Service (WMS) layers, GeoJSON data, vectors and tile layers, while the database in this application uses MySQL. The results of this GIS-based asset management system design research can be used to store, collect, repair, process, control and monitoring assets so that asset management for activities that benefit the community can be optimally improved. For the maintenance and utilization of asset management applications, training is carried out for operators and supervisors, as well as system support personnel.
Data Mining Algorithm Testing For SAND Metaverse Forecasting Indri Tri Julianto; Dede Kurniadi; Muhammad Rikza Nashrulloh; Asri Mulyani
Journal of Applied Intelligent System Vol 7, No 3 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i3.7155

Abstract

Metaverse is a technology that allows us to buy virtual land. In the future life in the real world can be duplicated into the Metaverse to increase efficiency, effectiveness, and a world without being limited by space and time. To buy land in the Metaverse, one can be done by using SAND. SAND is a crypto asset from a game called The Sandbox which functions as a transaction tool where in that game we can buy land and build it for various purposes just like we can store our Non-Fungible Tokens there. Metaverse is a digital business that will promise in the future because it offers easy and fast transactions. This study aims to compare the exact algorithm for making predictions about the SAND cryptocurrency used to buy Metaverse land. 7 algorithms are being compared, namely Deep Learning, Linear Regression, Neural Networks, Support Vector Machines, Generalized Linear Models, Gaussian Process, and K-Nearest Neighbors. The research method used is Knowledge Discovery in Databases. The research results show that the Support Vector Machines Algorithm has the most optimal Root Means Square Error value, root_mean_squared_error: 0.022 +/- 0.062 (micro average: 0.062 +/- 0.000). Based on this comparison, the Support Vector Machines Algorithm is suitable for predicting SAND Metaverse prices.
DATA MINING CLUSTERING FOOD EXPENDITURE IN INDONESIA Indri Tri Julianto; Dede Kurniadi; Muhammad Rikza Nashrulloh; Asri Mulyani
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.6.331

Abstract

The availability of food in a country is determined by a conducive climate. Prolonged droughts, floods, and natural disasters, especially for food crop production areas, will have an impact on the availability of natural disaster conditions faced by all countries including Indonesia is the Covid-19 pandemic, where this will affect food security in Indonesia. Data mining is the process of discovering the hidden meaning of a very large data set. The technique used in this study is Data Mining Clustering and the validity index used is Davies-Bouldin. This study aims to determine the Food Security Strategy in Indonesia through the Data Mining Clustering process based on food expenditure data and the Indonesian people's food expenditure per capita. The methodology used is Cross Industry Standard for Data Mining using the K-Means and K-Medoids Algorithm. The best cluster for the K-Means Algorithm is K=7 with a value of 0.341 and for the K-Medoids Algorithm, it is K=7 with a value of 0.362. This research produces the best algorithm, namely K-Means with a value of 0.341, which has a smaller value than K-Medoids with a value of 0.362. The results showed that the regional. cluster with the highest average expenditure on food was cluster 5 covering the DKI Jakarta area, while the cluster with the lowest expenditure was cluster 6 covering Central Java, East Nusa Tenggara, Southeast Sulawesi, Gorontalo, and West Sulawesi. In cluster 6, it is necessary to implement a strategy to increase food security by increasing production capacity and food reserves in each region.
Pemetaan Karakteristik Mahasiswa Penerima Kartu Indonesia Pintar Kuliah (KIP-K) menggunakan Algoritma K-Means++ Fitri Nuraeni; Dede Kurniadi; Gisna Fauzian Dermawan
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 11, No 3 (2022): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v11i3.1439

Abstract

Pengetahuan baru mengenai pemetaan karakteristik mahasiswa penerima KIP-K pada perguruan tinggi dapat menggunakan penggalian data yaitu teknik clustering. Pemetaan karakteristik ini dilakukan dari hasil pengelompokan mahasiswa berdasarkan atribut akademik dan non-akademik menggunakan algoritma K-Means++ yang dapat menurunkan jumlah perulangan dalam proses pengelompokan datanya. Dengan menggunakan metode Cross-Industry Standard Process for Data Mining (CRIPS-DM) dan algoritma clustering yaitu k-means++. Dari penelitian ini, dihasilkan model clustering dengan nilai k=2 berdasarkan grafik metode elbow dengan  nilai silhouette coefficient terbesar yaitu 0.7523 dan davies bouldine index (DBI) terkecil yaitu 0.49053. Dari hasil pemetaan karakteristik mahasiswa penerima KIP-K ini, didapatkan pengetahuan yang dapat menjadi bahan pengambilan keputusan perguruan tinggi penyelenggaran dalam penyeleksian pendaftar KIP-K sehingga meminimalisir masalah akademik mahasiswa penerima KIP-K di kemudian hari.
PENGEMBANGAN APLIKASI INDIKATOR STRATEGIS GARUT BERBASIS ANDROID PADA BADAN PUSAT STATISTIK KABUPATEN GARUT Dede Kurniadi; Muhammad Abdul Yusup Hanifah; Indra Trisna Raharja; Asri Mulyani
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 1 No 2 (2022): IT-Explore Juni 2022
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (672.022 KB) | DOI: 10.24246/itexplore.v1i2.2022.pp133-144

Abstract

Peranan Badan Pusat Statistik (BPS) adalah menyediakan kebutuhan data bagi pemerintah dan masyarakat. Data ini didapatkan dari sensus atau survei yang dilakukan sendiri dan juga dari departemen atau lembaga pemerintahan lainnya sebagai data sekunder. Data ini bisa dilihat dalam bentuk informasi di website resmi BPS, tetapi informasi yang disajikan belum lengkap karena terdapat data-data lain yang belum bisa di tampilkan khususnya untuk BPS Kabupaten Garut. BPS dibagi-bagi kedalam beberapa seksi untuk memenuhi tugas atau peranannya, dan yang menangani tugas diatas adalah seksi IPDS (Integrasi Pengolahan Diseminasi Statistik). Tujuan penelitian ini adalah merancang Aplikasi Indikator Strategis Garut Berbasis Android. Metode yang digunakan adalah Unified Approach dengan tahapannya yaitu Analisis, Desain, dan Kontruksi, sedangkan untuk pemodelannya menggunakan Unified Modelling Language. Penelitian ini menghasilkan sebuah perancangan aplikasi indikator strategis garut berbasis android yang diharapkan dapat digunakan dengan baik dan mampu membantu kinerja bidang IPDS menjadi lebih efektif dan efisien.
Pengembangan Sistem Informasi Monitoring Dan Pelaporan Kinerja Karyawan Perusahaan Menggunakan Balanced Scorecard Dan Scrum Dede Kurniadi; Ridwan Setiawan; Gelar Panca Ginanjar
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 1 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i1.3168

Abstract

This research aims to develop a website-based information system for monitoring and reporting company employee performance to assist companies in managing employee performance appraisals. This research case study was conducted in a company engaged in home marketing in Garut Regency, West Java, Indonesia. The system development is carried out by applying the Balanced Scorecard (BSC) method to evaluate employee performance based on four perspectives: financial perspective, customer perspective, internal business process perspective, learning, and growth perspective. In contrast, the software development methodology uses Agile with the Scrum approach with four stages: requirements, product backlog, sprint planning, sprint, and sprint review. The modeling used in system design is the Unified Modeling Language (UML), then for testing the system software using the BlackBox testing method. The results of this study are a website-based information system for monitoring and reporting company employee performance that companies can use for monitoring and managing employee performance appraisals and displaying information on the results of each employee's performance appraisal.
Alternative Text Pre-Processing using Chat GPT Open AI Indri Tri Julianto; Dede Kurniadi; Yosep Septiana; Ade Sutedi
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 1 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i1.59746

Abstract

Text Pre-Processing is the first step in Sentiment Analysis. Categorizing a sentiment in a dataset is part of the Text-Preprocessing stage to get the optimal model accuracy value. Generative Pretrained Transformer, often known as Chat GPT, is a Machine Learning model that can automatically generate realistic and meaningful text. This study aims to examine the capability of GPT Chat as an alternative in the Text-Pre-Processing stage by utilizing GPT Chat 3 from the openai.com website in the Text-Pre-Processing stage of the collected tweet data. The data used in this research is the result of crawling Twitter by inserting the keyword "Chat GPT”. This study method was carried out by measuring performance using the K-Nearest Neighbor and Naïve Bayes Algorithms to find the best performance value and compare it with the Text-Preprocessing generated by Rapidminer. It is shown that the performance accuracy produced using the K-Nearest Neighbor Algorithm is 73.57% using the Linear Sampling method. The comparison result with the Text-Preprocessing method using Rapidminer indeed shows a better accuracy of 75.33%, which means it has a narrow difference of 1.76% with the Chat GPT Text Pre-Processing method. However, both are still in the same category, which is Fair Classification. The results of this research show that Chat GPT can be an alternative in Text-Preprocessing datasets for sentiment analysis.
Rekomendasi Pemilihan Program Studi Menggunakan Algoritma Naïve Bayes Hamzah Nurrifqi Fakhri Fikrillah; Dede Kurniadi
Jurnal Algoritma Vol 20 No 1 (2023): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.20-1.1236

Abstract

The purpose of this study is to provide recommendations for selecting study programs for prospective students who will enter the Garut Institute of Technology (ITG). The results of this recommendation will provide information on study programs that are in accordance with the academic value of the prospective student. To achieve this goal, this study uses the Naïve Bayes Algorithm to predict future opportunities based on past data, then to get recommendation results by finding the greatest probability value for each attribute. The stages of the algorithm carried out include data collection, data processing, modeling, and evaluation. The data used for analysis needs to use data that corresponds to the Final Grades of Garut College of Technology students during their school years from 2014 to 2019 with a total of 30 data in each study program with a total of 90 data. From the four modeling times data and algorithm testing resulted in the best Naïve Bayes algorithm calculation accuracy with an accuracy of 73.4%.
Perancangan Sistem Informasi Monitoring dan Pelaporan Kinerja Aparatur Sipil Negara Berbasis Web dan Android Ridwan Setiawan; Dede Kurniadi; Yayat Supriatna
Jurnal Algoritma Vol 20 No 1 (2023): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.20-1.1281

Abstract

The large number of activities and the number of State Civil Apparatus (ASN) that must be reported makes it difficult for the government to evaluate the performance of workers. The purpose of this study was to design an information system for monitoring and reporting ASN performance in Leles District using the Scrum methodology where this technique can be used for overall system development, partial system development, and internal/customer projects. With the results of the system in the form of designing web-based and Android applications. The modeling uses the unified modeling language (UML), namely use cases, class diagrams, interfaces and prototypes. This research is only limited to the performance report of the state civil apparatus until the validation process by the supervisor until the report is complete. Using this information and monitoring system can reduce the time it takes to send physical evidence of ASN performance to supervisors and reduce the possibility of errors or lack of reporting evidence.
Prediction System for Problem Students using k-Nearest Neighbor and Strength and Difficulties Questionnaire Dede Kurniadi; Asri Mulyani; Inda Muliana
JOIN (Jurnal Online Informatika) Vol 6 No 1 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i1.701

Abstract

The student counseling process is the spearhead of character development proclaimed by the government through education regulation number 20 of 2018 concerning strengthening character education. Counseling at the secondary school level carries out to attend to these problems that might resolve with a decision support system. So that makes research challenging to measure completion on target because it is not doing based on data. The counseling teacher does not know about student's mental and emotional health conditions, so it is often wrong to handle them. Therefore, we need a system that can recognize conditions and provide recommendations for managing problems and predicting students who have potential issues. The Algorithm used to predict problem students is K-Nearest Neighbor with a dataset of 100 students. The stages of predictive calculation are data collection, data cleaning, simulation, and accuracy evaluation. Meanwhile, building the system is done using the rapid application development methodology where the instrument used to map the student's condition is the Strenght and Difficulties Questionaire instrument. This research is a system to predict problem students with an accuracy rate of 83%. The level of user experience based on the User Experience Questionnaire (UEQ) results in the conclusion that the system reaches "Above Average.". This system is expecting to help counseling teachers implement an early warning system, help students know learning modalities, and help parents recognize the child's personality better.
Co-Authors Abania, Nia Abdulah, Farhan Naufal Abdurrahman, Fauzan Abdussalam, Iqbal Abdussalam Abdusy Syakur Amin Ade Sutedi Ade Sutedi Ade Sutedi, Ade Adiwangsa, Alfian Akmal Agus Hermawan Agus Nugraha Agustiansyah, Yoga Ahmad Habib Lutfi Aisyah Fitri Islami Ajif, Arvin Muhammad Ajiz, Rafi Nurkholiq Akbar, Gugun Geusan Alamsyah, Renaldy Aldy Rialdy Atmadja Ali Djamhuri Alisha Fauzia, Fathia Alkamal, Chaerulsyah Alvin Zainal Musthafa Alwan Nul Hakim Amrulloh, Muhammad Fawaz Andri Saepuloh Aneu Suci Nurjanah Asri Indah Pertiwi Asri Mulyani Asri Rahayu Ningsih Ayu Suryani B. Balilo Jr , Benedicto B. Balilo Jr, Benedicto Balilo Jr, Benedicto B. Barlinti Maryam Budik Burhanuddin, Ridwan Cahya Mutiara Dede Sopiah Della Adelia Anugrah Detila Rostilawati Dewi Tresnawati Dhea Arynie Noor Annisa Diar Nur Rizky Diaz Radhian Salam Diazki, Moch Haiqal Diki Jaelani Dini Destiani Siti Fatimah Diva Nuratnika Rahayu Dudy Mohammad Arifin Dyka Afan Afthori Dzikri Nursyaban Efi Sofiah Elsen, Rickard Eri Satria Erick Fernando B311087192 Erwan Yani Erwan Yani, Erwan Erwin Gunadhi Rahayu, Raden Erwin Widianto Fadillah, Hadi Bagus Faisal, Ridwan Nur Fajar Rahman Faturrohman, Nadhif Fauziah, Fathia Alisha Fauziyah, Asyifa Fikri Zakaria Rahman Firmansyah, Marshal Fitri Nuraeni Fitriani, Ranti Fitriyani Gelar Panca Ginanjar Ghilman Hasbi Basith Gisna Fauzian Dermawan H. Bunyamin Hadi Wijaya, Tryana Haekal, Mohamad Fikri Hamzah Nurrifqi Fakhri Fikrillah Hari Ilham Nur Akbar Hasfi Syahrul Ramadhan Hazar, Aura Fitria Helmalia P, Nabilla Febriani Hendri Aji Pangestu Heri Johari Heri Suhendar Heri Suhendar Hilmi Aulawi Ida Farida Ikbal Lukmanul Hakim Ikhrom, Taufik Darul Ikmal Muhammad Fadhil Ilham Muhamad Ramdan Imas Dewi Ariyanti Inda Muliana Indra Trisna Raharja Indri Tri Julianto Indri Tri Julianto Intan Sri Fatmalasari Irawan, Muhammad Randy Irfan Qusaeri Irfanov, Muhammad Irsyad Ahmad Iskandar, Joko Jajang Jaenudin Jajang Romansyah Jembar, Tegar Hanafi Khaerunisa, Nisrina Khoerunisa, Sarah Kusmayadi, Kusmayadi Latif, A. Abdul Latifah, Ayu Leni Fitriani Leni Fitriani, Leni Lia Amelia Lindayani, Lindayani M. Mesa Fauzi Mahendra Akbar Musadad Maulana , Muhammad Arief Maulana, Ahmad Rakha Maulana, Ilham Ahmad Maulana, Yusep Maulina, Wina Senja Meta Regita Mochamad Deni Ramdani Muhamad Solihin Muhammad Abdul Yusup Hanifah Muhammad Affan Al Sidqi Muhammad Rikza Nashrulloh Muhammad Saleh Muhammad Sanusi Muhammad Wildan Muliana, Inda Muttaqin, Moch Riefky Chaerul Nita Nurliawati Nugraha, M Aldi Nugraha, Nikolas Pranata Nurfadillah, Rifa Sri Nurhaliza, Nabila Putri Nurlisina, Elisa Nurpatmah, Lisna Nursa'diah, Rifania Sapta Nursyaban, Dzikri Nurul Fauziah Nurul Khumaida Nurzaman, Muhammad Zein Omar Komarudin Pratama, Reifalga Gais Prayoga, Moch. Gumelar Putri, Mita Hidayani Raharja, Indra Trisna Rahayu, Diva Nuratnika Rahayu, Raden Erwin Gunadhi Rahmat, Agil Rahmi, Murni Lestari Rajab, Ilham Syahidatul Ramdhan, Dekha Ramdhani Hidayat Randy Wardan Ridwan Setiawan Ridwan Setiawan Ridwan Setiawan Ridwan Setiawan Rifky Muhammad Shidiq Rinda Cahyana Rinda Cahyana Risfiyanisa Fasha Rizki Fauziah Roeri Fajri Firdaus Rohman, Fauza Rohmanto, Ricky Rostina Sundayana Rubi Setiawan Rudi Sutrio Safei P, M Iqbal Ismail Sarah Khoerunisa Sermana, Elsa Maharani Sheny Puspita Indriyani Siti Rima Fauziyah Sofwan Hamdan Fikri Sopiah, Dede Sri Intan Multajam Sri Mulyani Lestari Sri Rahayu SRI RAHAYU Sri Rahayu Syahrul Sidiq Syaiffani, Moch Assami Tina Maryana Undang Indrajaya W, Faksi Ahmad Wahidah, Tania Agusviani Wiwit Septiani Yanti Sofiyanti Yayat Supriatna Yoga Handoko Agustin Yosep Septiana Yosep Septiana Yuni Yuliani Yusfar Ilhaqul Choer Yusuf Mauluddin Zaqiah, Neng Nufus Zulkarnaen, Ade Iskandar