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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Computatio : Journal of Computer Science and Information Systems Jurnal Akuntansi Jurnal CoreIT Jurnal Komputasi Network Engineering Research Operation [NERO] BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Teknoinfo Krea-TIF: Jurnal Teknik Informatika Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Madani Jurnal Tekno Kompak Building of Informatics, Technology and Science Jurnal Sistem informasi dan informatika (SIMIKA) Journal of Computer System and Informatics (JoSYC) JiTEKH (Jurnal Ilmiah Teknologi Harapan) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Mattawang: Jurnal Pengabdian Masyarakat JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Ilmiah Infrastruktur Teknologi Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Jurnal Pendidikan dan Teknologi Indonesia KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Telematics and Information Technology (TELEFORTECH) Jurnal Ilmiah Sistem Informasi Akuntansi (JIMASIA) Paradigma Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Edutic : Pendidikan dan Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Bulletin of Informatics and Data Science Jurnal Ilmiah Computer Science Jurnal Informatika dan Rekayasa Perangkat Lunak Journal of Social Sciences and Technology for Community Service
Dedi Darwis
Universitas Teknokrat Indonesia
Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Implementasi Sensor Gas Amonia Berbasis Internet of Things pada Peternakan Ayam Potong dengan Sistem Monitoring dan Pengendalian Kualitas Udara Otomatis: Implementation of Internet of Things-Based Ammonia Gas Sensors on Broiler Chicken Farms with an Automatic Air Quality Monitoring and Control System Budiawan, Aditia; Suryono, Ryan Randy; Darwis, Dedi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 1 (2025): MALCOM January 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i1.1649

Abstract

Penelitian ini bertujuan untuk mengembangkan dan mengimplementasikan sensor gas amonia berbasis IoT pada peternakan ayam potong dengan sistem monitoring dan pengendalian kualitas udara otomatis. Sistem ini menggunakan sensor MQ137 untuk mendeteksi kadar gas amonia dan mikrokontroler ESP32 untuk mengontrol berbagai komponen seperti buzzer, RTC DS1307, dan sistem penyemprotan otomatis. Data kualitas udara dikumpulkan dan dipantau secara real-time melalui aplikasi web, memungkinkan peternak untuk mengambil tindakan cepat dalam menjaga kondisi optimal di kandang ayam. Hasil penelitian menunjukkan bahwa sistem ini efektif dalam mengendalikan kadar gas amonia, dengan penyemprotan air otomatis yang diaktifkan ketika kadar gas melebihi ambang batas 7,2 ppm, sehingga meningkatkan kesehatan dan produktivitas ayam potong
Penerapan Platform DigiLearnHub Untuk Meningkatkan Kemampuan Literasi dan Numerasi Siswa Serta Pelayanan Administrasi di SMAS Kesuma Bakti Priandika, Adhie Thyo; Saputra, Very Hendra; An’ars, M. Ghufroni; Darwis, Dedi
Journal of Social Sciences and Technology for Community Service (JSSTCS) Vol 5, No 2 (2024): Volume 5, Nomor 2, September 2024
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jsstcs.v5i2.4731

Abstract

SMAS Kesuma Bakti Bekri (SMAS-KBB) has several priority issues that need to be addressed, includingThe educational report score for literacy skills at SMAS-KBB is still categorized as poor, with a score of 33.33. The educational report score for numeracy skills at SMAS-KBB is also categorized as poor, with a score of 35.56. The management of the school's administrative and financial services system has not been optimized. Based on these priority issues, the proposed solutions areImplementing the DigiLearnHub Platform. Providing training and assistance to teachers in creating and designing engaging literacy and numeracy learning content to be used on the DigiLearnHub platform. Providing training and assistance to students on the use of the DigiLearnHub platform and the importance of improving literacy and numeracy learning as a foundation for mastering science and technology. Implementing an application that can be accessed digitally through the website and mobile for real-time School Administration System. Based on the evaluation results, there was an 84.85% improvement in teachers' ability to create literacy and numeracy learning content. Furthermore, based on post-test results conducted with students, there was an increase in the average student score, with literacy achieving 87.2 and numeracy 86.2. This indicates an improvement in literacy and numeracy learning quality, which is expected to prepare students for the ANBK. In addition, the school administrative service application has improved the school's administrative and financial services. According to survey results, 87% of students and parents expressed high satisfaction with the services provided through the school administration and financial application.
Implementasi Smart Cow Farming Technology untuk Monitoring Pertumbuhan Sapi dan Peningkatan Skala Usaha pada Kelompok Peternak Sapi DiBa Farm Kabupaten Lampung Selatan Pasaribu, A Ferico Octaviansyah; Saputra, Febrian Eko; Wati, Novi Eka; Darwis, Dedi
Journal of Social Sciences and Technology for Community Service (JSSTCS) Vol 5, No 2 (2024): Volume 5, Nomor 2, September 2024
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jsstcs.v5i2.4730

Abstract

The DiBa Farm Livestock Group faces several urgent issues that need to be addressed not all cows can achieve the minimum weight gain target of 1.5 kg per day. Monitoring of cow growth productivity is done manually; (3) the calculation of the cost of goods sold is also still done manually. The partner's marketing of livestock products is limited to regular customers within the South Lampung area only. Based on these prioritized issues, the proposed solutions and methods are implementing a wheelbarrow tool with a digital scale for transporting cow feed using IoT technology. Implementing a cow growth monitoring application using RFID. Implementing a website-based application to automatically determine the Cost of Goods Sold for cows. Implementing a digital marketing application for cow sales that can be accessed via a website. Providing training and assistance related to digital marketing strategies. Based on the evaluation results, it was found that the implementation of Smart Cow Farming Technology 100% improved the partners' knowledge of its usage. The average cow growth increased monthly, from 45 kg to 50 kg. Additionally, there was a 25% increase in profits due to the implementation of the cost of goods sold calculation application and the online cow sales application. The evaluation results from the digital marketing training activities also showed that 85% of the partners' knowledge and understanding improved in terms of using digital marketing.
PERBANDINGAN METODE NAÏVE BAYES DAN SVM UNTUK SENTIMEN ANALISIS MASYARAKAT TERHADAP SERANGAN RANSOMWARE PADA DATA KIP-K Ramadan, Nabil Safiq; Darwis, Dedi
Jurnal Sistem Informasi dan Informatika (Simika) Vol 8 No 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3621

Abstract

This research examines ransomware attacks on KIP-K data by analyzing the opinions of Social Media X users, using the naïve bayes classifier (NBC) and support vector machine (SVM) methods. The rapid development of technology not only brings great benefits but also increases the risk of digital attacks by certain parties. One example is a ransomware attack that caused a KIP-K data leak. In this study, sentiment analysis was applied to identify public opinions or responses obtained from Social Media X, with the help of python programming and google colab. Of the total 2,648 raw data collected, pre-processing was carried out resulting in 1,738 cleaned data. The study compared two methods, namely Naïve Bayes and Support Vector Machine, to determine what method is more effective in analyzing public sentiment related to ransomware attacks on KIP-K data. The focus of this study is to understand the percentage of Social Media X users' comments and responses related to the KIP-K ransomware taken from media sosial X. The stages of sentiment analysis in this study include crawling, labeling, preprocessing, method classification, and visualization. Before the classification process was carried out, the data was divided into two parts, namely 30% for test data and 70% for training data. Data labeling resulted in 1,313 negative data, 957 positive data and 377 neutral data. The classification results show that the NBC method has an accuracy of 70%, while the SVsM achieves an accuracy of 88%. Based on these results, SVM is proven to be superior in data analysis compared to NBC, especially for big data.
Optimizing Employee Admission Selection Using G2M Weighting and MOORA Method Rahmanto, Yuri; Wang, Junhai; Setiawansyah, Setiawansyah; Yudhistira, Aditia; Darwis, Dedi; Suryono, Ryan Randy
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 1 (2025): March 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i1.8224

Abstract

An objective and effective employee admission selection process is a crucial step for the success of the organization in achieving its goals. Problems in employee recruitment selection often arise due to a lack of good planning and system implementation, namely decisions are often influenced by personal preferences, stereotypes, or non-relevant factors, thus reducing objectivity in choosing the best candidates. Objective selection ensures that candidate assessments are conducted based on measurable, relevant, and bias-free criteria, so that only individuals who truly meet the company's needs and standards are accepted. The purpose of developing an optimal approach in employee admission selection using G2M weighting and MOORA is to create a more objective, efficient, and accurate selection process. This approach aims to integrate the calculation of criterion weights mathematically, such as those offered by G2M, in order to eliminate subjective bias in determining criterion prioritization. The MOORA method of evaluating alternative candidates is carried out through ratio analysis that takes into account various criteria simultaneously, resulting in a transparent and data-driven ranking. The results of the employee admission selection ranking based on the criteria that have been evaluated, Candidate 3 obtained the highest score of 0.4177, indicating that this candidate best meets the expected criteria. The second position was occupied by Candidate 6 with a score of 0.3886, followed by Candidate 9 with a score of 0.3528. This research contributes to the recruitment process, by providing a more reliable, transparent, and less subjective way of selecting the right candidates for the positions that companies need.
Perbandingan Algoritma NBC, SVM dan Random Forest untuk Analisis Sentimen Implementasi Starlink pada Media Sosial X Kencono, Lintang; Darwis, Dedi
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6813

Abstract

Internet development in Indonesia continues to progress rapidly, but equitable access remains a challenge, especially in remote areas. Starlink, a satellite internet service from SpaceX, comes as a solution to reduce this gap by providing fast and stable connectivity. This research analyzes public sentiment towards the implementation of Starlink on social media platform X through a comparative approach using three Machine Learning algorithms: Naive Bayes Classifier, Support Vector Machine, and Random Forest. The research data consisted of 6,780 Indonesian tweets collected during the period September 1 to November 30, 2024 using the harvest tweet library with the keywords “starlink,” “internet starlink,” and “SpaceX starlink”. After preprocessing, a total of 5,382 tweets were used, consisting of 4,348 tweets with negative sentiment and 884 tweets with positive sentiment. To overcome data imbalance, Synthetic Minority Over-sampling Technique (SMOTE) was applied. Before the application of SMOTE, the Random Forest model showed the highest accuracy of 92%, followed by Support Vector Machine with 91%, and Naive Bayes Classifier with 85%. After SMOTE was applied, the accuracy of the three models increased significantly, with Random Forest reaching 99%, Support Vector Machine 98%, and Naive Bayes Classifier 91%. Random Forest also showed the best performance in detecting positive sentiment, with Precision and Recall values reaching 100%. This research provides an in-depth insight into the effectiveness of Machine Learning algorithms in analyzing public sentiment towards Starlink services on social media and shows that the application of SMOTE can improve the model's performance in classifying sentiment more evenly.
Perbandingan Metode SVM dan Naïve Bayes untuk Analisis Sentimen pada Twitter tentang Obesitas di Kalangan Gen Z Maulana, Nanda Arif; Darwis, Dedi
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 3 (2025): JPTI - Maret 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.691

Abstract

Pada era digital yang berkembang pesat, media sosial menjadi platform utama dalam menyampaikan opini dan informasi, termasuk mengenai isu kesehatan seperti obesitas, terutama di kalangan Generasi Z. Penelitian ini bertujuan untuk membandingkan efektivitas metode Support Vector Machine (SVM) dan Naïve Bayes dalam mengklasifikasikan sentimen publik terhadap obesitas berdasarkan data dari Twitter. Dataset penelitian terdiri dari 4.056 tweet yang dikumpulkan melalui proses scraping, kemudian diproses melalui tahap pembersihan, tokenisasi, dan stemming. Hasil penelitian menunjukkan bahwa algoritma SVM memiliki akurasi 89,23%, lebih tinggi dibandingkan dengan Naïve Bayes yang hanya mencapai akurasi 72,14%. Keunggulan SVM terletak pada kemampuannya memisahkan kelas sentimen dengan hyperplane optimal, menjadikannya metode yang lebih andal untuk analisis sentimen berbasis media sosial. Hasil penelitian ini dapat membantu pembuat kebijakan kesehatan dalam memahami opini publik terhadap obesitas serta merancang strategi komunikasi yang lebih efektif berbasis data.
Combination of MEREC and WASPAS Methods for Performance Assessment in the Decision Support System for Member Admission for the Metaverse Team Putra, Ade Dwi; Rahmanto, Yuri; Darwis, Dedi; Aldino, Ahmad Ari; Setiawansyah, Setiawansyah
Bulletin of Informatics and Data Science Vol 3, No 1 (2024): May 2024
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v3i1.100

Abstract

The selection of the right team members is critical to the success of complex and multidisciplinary Metaverse projects, the previous method used in this selection employed criteria weights based on individual evaluator assessments.. This study proposes the application of a combination of MEREC (method based on the removal effects of criteria) and WASPAS (weighted aggregated sum product assessment) methods to build a DSS in the selection of Metaverse team members. The MEREC method is used to determine the weight of relevant criteria, such as technical skills, communication, innovation, problem-solving, team collaboration, and experience. Meanwhile, the WASPAS method is used to rank candidates based on evaluation scores calculated using a combination of the Weighted Sum Model (WSM) and the Weighted Product Model (WPM). The results showed that the candidate with the highest score was Member Candidate 5 with a score of 0.9806, followed by Member Candidate 11 with a score of 0.944 and Member Candidate 9 with a score of 0.9433. This research proves that the combination of MEREC and WASPAS methods can be used effectively to select team members who have the best quality and are in accordance with the needs of Metaverse projects. A major contribution of this research is the development of a more objective and structured method for the selection of team members in technology projects that require multidisciplinary expertise
Peran Load Balancing Dalam Meningkatkan Kinerja Web Server Di Lingkungan Cloud Riskiono, Sampurna Dadi; Darwis, Dedi
Krea-TIF: Jurnal Teknik Informatika Vol 8 No 2 (2020)
Publisher : Fakultas Teknik dan Sains, Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/kreatif.v8i2.3503

Abstract

Semakin pesatnya pertumbuhan internet saat ini, berdampak pada meningkatnya akses pengguna yang terhubung didalamnya. Hal tersebut berpengaruh pada kebutuhan terhadap mesin penyedia layanan, seperti halnya server Web server. Hadirnya teknologi cloud saat ini, sangat membantu para pengelola Web server dalam melalukan manajerial Web server khusnya terhadap mesin server yang digunakan. Namun dalam upaya untuk meningkatkan kinerja sebuah Web server yang mengalami peningkatan akses oleh penggunanya, maka diperlukan evaluasi terhadap penerapan metode load balancing dalam mendukung peningkatan kinerja di lingkungan cloud. Sehingga beban koneksi yang masuk untuk meminta layanan Web server tersebut dapat dilayani oleh beberapa server yang menyediakan konten Web server yang sama. Sehingga dengan hal tersebut, maka kinerja dari sebuah webserver dapat terus di pertahankan bahkan mungkin di tingkatkan. Oleh karena itu diperlukan penelitian untuk merancang bagaimana membangun sistem server yang dapat menangani banyaknya permintaan layanan yang masuk agar beban dari server dapat diatasi. Hal ini bertujuan untuk meningkatkan layanan yang dapat diberikan oleh server penyedia kepada penggunanya. Salah satu yang dapat diupayakan untuk mengatasi permasalahan tersebut adalah penggunaan banyak server. Sehingga dengan metode load balancing maka distribusi beban dapat seimbang dan merata ke masing-masing server. Akhirnya dengan implementasi load balancing, maka kinerja layanan Web server yang berada dalam lingkungan cloud dapat terus ditingkatkan.
COMPARISON OF EDGE DETECTION METHODS USING ROBERTS AND LAPLACIAN OPERATORS ON MANGO LEAF OBJECTS Darwis, Dedi; Fernando, Yusra; Trisnawati, Fika; Marzuki, Dwiki Hafizh; Setiawansyah, Setiawansyah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1815-1824

Abstract

Edge detection is a technique to find the outlines of an object in an image by detecting significant changes in brightness or discontinuities. This study discusses the comparison of edge detection using Roberts operators and Laplacian operators. The object used in edge detection is four types of mango leaves (Golek, Arum Manis, Madu, and Kuweni) with the *.jpeg format that has been pre-processed with 1000 x 278 pixels. The test used in this study compared the results of White Pixel values, MSE, and PSNR with test data as many as 24 data samples from four types of mango leaves. The results of the comparison of edge detection methods using the Laplacian operator get the lowest MSE value of 7.8577, the highest PSNR value of 39.2119, and the white pixel value of 164951, while the Roberts operator gets the lowest MSE value of 8.9723, the highest PSNR value of 38.6358, and the white pixel value of 155889.
Co-Authors . Yuniarwati ., Rusliyawati Abhishek R Mehta Abhishek R Mehta Ade Dwi Putra Ade Surahman Ade Surahman Adhie Thyo Priandika Aditia Yudhistira Agung Saputra Agus Wantoro Agustina, Intan Ahmad Ari Aldino Ahmad Ari Aldino Ahmad Suhendri Aidil Akbar Alita, Debby Andi Ilham Rahmansyah Andre Setiawan Andri Pramuditya An’ars, M. Ghufroni Aprian Nuriansah Ariany, Fenty Arie Qur’ania Aulia Mustika Sari Ayu Vidiasari Bambang Dwi Setyarto Bayu Dwi Juniansyah Budiawan, Aditia Chaswarina Nimas Maharani Cici Dian Paramita Damayanti, Damayanti Dartono Dartnono Dartono Dartono Dartono Dartono Depriansah Depriansah Dini Wahyuni Ditha Nurjayanti Dwi Andika Dwi Maila Pauristina Dwi Rahma Sari Eka Shintya Pratiwi Elvano Delisa Mega Endi Febrianto Fadila Shely Amalia Fahri Hanif Fatmawati Isnain Fernando, Yusra Fikri Hamidy Gunawan, Rakhmat Dedi Heni Sulistiani I Gede Heri Susanto Ichtiar Lazuardi Putra Ikbal Yasin Ilham Muhammad Ghoffar Ilham Utama Putra Imam Ahmad Ismail, Izudin Ismail, Izzudin Isnain, Auliya Rahman Kencono, Lintang Khoirunnisa, Yosi Kisworo Kisworo Kisworo KISWORO Lusiana Indawa M Joko Priono Maria Ainun Nazar Marzuki, Dwiki Hafizh Maulana, Nanda Arif Megawaty, Dyah Ayu Meylinda Meylinda Mirza Wijaya Putra Muhammad Bakri Muhammad Fauzan Ramadhani Muhammad Khotimul Anwar Nova Evrilia Novi Eka Wati Nugraha Ashari Nurhuda Budi Pamungkas Nurhuda Budi Pamungkas Nurul Hotimah Pasaribu, A. Ferico Octaviansyah Prabowo, Rizky Pramita, Galuh Prastowo, Agung Tri Priandika, Adhie Thyo Prita Dellia Purnomo Aji Putra, Ade Dwi Putri Lestari Rachmad Nugroho Ramadan, Nabil Safiq Rayin Biilmilah Rika Mersita Riskiono, Sampurna Dadi Ryan Randy Suryono Saefulloh Saefulloh Salsa Safhira Sampurna Dadi Riskiono Sanriomi Sintaro Saputra, Febrian Eko Sari, Priskila Lovika Setiawansyah Setiawansyah Sitna Hajar Hadad suaidah suaidah Surahman, Ade Surya Indra Gunawan Tika Yusiana Tithania Marta Putri Trisnawati, Fika Ummy Permata Hakim Vera Herlinda Very Hendra Very Hendra Saputra Very Hendra Saputra Very Hendra Saputra Wamiliana Wamiliana Wang, Junhai Waqas Arshad, Muhammad Wayan Kresna Yogi Swara yasin, ikbal Yuri Rahmanto Yusra Fernando Yusra Fernando Yusri Kusumayuda