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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) TIN: TERAPAN INFORMATIKA NUSANTARA 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 JEECS (Journal of Electrical Engineering and Computer Sciences) 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
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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.
Optimizing E-Commerce Platform Selection Using Root Assessment Method and MEREC Weighting Wang, Junhai; Darwis, Dedi; Gunawan, Rakhmat Dedi; Ariany, Fenty
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.6

Abstract

The number of users of e-commerce platforms has increased significantly in recent years, and consumers are now more likely to shop online due to ease of access, diverse product choices, and flexibility in transaction times. The difficulty in determining the best e-commerce platform is often caused by subjectivity in the weighting of the criteria used for evaluation. The weighting process is carried out based on the preferences of certain individuals or groups, without considering objective data. This research aims to apply an objective, structured, and accurate approach in evaluating and ranking e-commerce platforms based on relevant multi-dimensional criteria. By using the root assessment method, the evaluation process can be carried out systematically through hierarchical analysis, while the MEREC weighting ensures that the weight of each criterion reflects its real impact on the outcome of the decision. Through the combination of these two methods, this research is expected to make a significant contribution to improving the quality of decision-making, especially in helping users or business people choose the e-commerce platform that best suits their needs. The results of the final score calculation Platform E was ranked first with the highest score of 4.87083, Platform A was ranked second with a score of 4.85162, and Platform B was ranked third with a score of 4.83842. Future research should address the identified limitations by exploring the integration of advanced predictive analytics and artificial intelligence techniques to improve the adaptability and resilience of models. In addition, sensitivity analysis of the MEREC Root Assessment and Weighting Methods should be performed to understand its performance under various data conditions.
Penerapan Aplikasi FashionFleet Berbasis AI untuk Meningkatkan Omset Penjualan dan Pengelolaan Manajemen Usaha Fernando, Yusra; Darwis, Dedi; Saputra, Febrian Eko
Journal Social Science And Technology For Community Service Vol. 6 No. 2 (2025): Volume 6, Nomor 2, September 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jsstcs.v6i2.863

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kinerja usaha UMKM Toko Baju Starly melalui penerapan aplikasi FashionFleet berbasis Artificial Intelligence (AI). Permasalahan utama mitra meliputi pencatatan transaksi yang masih manual, kesulitan dalam perhitungan harga pokok penjualan (HPP), kurangnya pengelolaan stok, serta strategi pemasaran digital yang belum optimal. Metode yang digunakan dalam kegiatan ini mencakup pendekatan partisipatif, kolaboratif, dan pemberdayaan melalui pelatihan, pendampingan, serta implementasi aplikasi. Hasil evaluasi menunjukkan adanya peningkatan signifikan pada berbagai aspek, antara lain pemahaman penggunaan aplikasi (45% menjadi 85%), manajemen stok (40% menjadi 90%), pencatatan transaksi (50% menjadi 92%), strategi pemasaran digital (35% menjadi 88%), serta pengelolaan keuangan (48% menjadi 90%). Selain itu, omset penjualan juga meningkat sekitar 56% setelah penggunaan aplikasi. Dengan demikian, penerapan FashionFleet terbukti mampu meningkatkan efisiensi operasional, memperluas jangkauan pasar, dan mendukung kemandirian mitra dalam menghadapi tantangan era digital.
Kombinasi Gifshuffle, Enkripsi AES dan Kompresi Data Huffman untuk Meningkatkan Keamanan Data Darwis, Dedi; Prabowo, Rizky; Hotimah, Nurul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 4: Agustus 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (131.453 KB) | DOI: 10.25126/jtiik.201854727

Abstract

Informasi merupakan hal yang sangat berharga. Saat informasi atau data jatuh ke tangan yang tidak bertanggung jawab, maka akan menjadi bencana bagi pemiliknya. Berbagai macam cara dilakukan untuk melindungi data atau informasi. Teknik pengamanan data atau informasi telah berkembang dengan sangat pesat. Proses pengamanan pesan yang banyak beredar diataranya dengan menggunakan kriptografi dan steganografi. Dua teknik ini merupakan teknik yang berbeda maksud dan tujuannya. Kriptografi bertujuan untuk mengacak pesan supaya sulit dibaca oleh pihak yang tidak berkepentingan. Sedangakan steganografi bertujuan untuk menyembunyikan pesan.Pada penelitian ini akan menggabungkan teknik kriptografi metode AES dengan teknik steganografi metode gifsuffle. Teknik kriptografi metode AES akan digunakan untuk merubah data atau informasi yang berbentuk plain-text menjadi cipher-text. Selanjutnya cipher-text tesebut akan disembunyikan ke dalam gambar berformat gif dengan metode steganografi gifsuffle. Metode gifsuffle  akan dikombinasikan dengan algoritma huffman untuk memperbanyak pesan yang dikirimkan.Penelitian ini berhasil menggabungkan metode kriptografi AES dengan metode steganografi gifsuffle. Hasil pengujian imperceptibility menunjukkan 85% responden tidak dapat membedakan gambar asli dengan gambar yang telah disisipi pesan. Pemisahan gambar dengan pesan dapat dilakukan dengan akurasi 100% dan proses dekripsi pesan cipher-text menjadi plain-text juga dapat dilakukan dengan sempurna. AbstractInformation is a very valuable thing. When information or data falls into irresponsible hands, it will be disastrous for the owner. A variety of ways are done to protect data or information. Data security techniques have grown very rapidly. The process of securing a message that many circulated diataranya by using cryptography and steganography. These two techniques are different techniques of purpose and purpose. Cryptography aims to randomize messages to be difficult to read by unauthorized parties. While steganografi aims to hide the message.In this research combine AES cryptographic method and with gifsuffle steganography method. AES method will be used to convert data or information in the plain-text form into cipher-text form. Furthermore, the cipher-text will be hidden into gif-format images with gifsuffle steganography method. The gifsuffle method will be combined with the huffman algorithm to multiply the transmitted data.This research successfully combined AES cryptography method with gifsuffle steganography method. Imperceptibility test results show 85% of respondents can not distinguish original images with images that have been inserted data. Separation of images with data can be well done and the accuration reach 100%. Process of decrypting cipher-text into plain-text can also be done perfectly.
Kombinasi Logarithmic Percentage Change-Driven Objective Weighting dan Complex Proportional Assessment Dalam Penentuan Supplier Perlengkapan Olahraga Pramuditya, Andri; Darwis, Dedi; Setiawansyah, Setiawansyah
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5160

Abstract

Sports equipment suppliers are suppliers that provide a wide range of equipment, clothing, and accessories needed by athletes, sports teams, and fans to support their activities. One of the main problems in choosing a sports equipment supplier is consistent product quality. Companies that choose sports equipment suppliers need to ensure that the products they buy meet the expected quality standards. In addition, the issue of stock availability and reliable delivery times is also a concern, as the inability to get goods on time can interfere with sports activities or businesses that depend on such equipment. The combination of Logarithmic Percentage Change-Driven Objective Weighting (LOPCOW) and Complex Proportional Assessment (COPRAS) provides a solid and adaptive framework for selecting sports equipment suppliers that best suit existing needs and priorities. This approach helps decision makers to make more informed and targeted decisions, taking into account the overall impact of each supplier's choice in the sporting goods industry. The results of supplier determination recommendations show the results of the assessment of each supplier, based on calculations using a combination of LOPCOW and COPRAS for the first rank with a utility value of 100 obtained by TRB Suppliers. The results of applying the combination of LOPCOW and COPRAS methods in supplier determination can provide more holistic and accurate results, the combination of these two methods can provide a more complete and detailed view of optimal supplier selection, taking into account dynamic changes in relevant criteria and preferences.
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.
Co-Authors . Yuniarwati ., Rusliyawati Abhishek R Mehta Ade Dwi Putra Ade Surahman Ade Surahman 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 An’ars, M. Ghufroni Aprian Nuriansah Ari Sulistiyawati 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 Febrianto, Endi Fernando, Yusra Fikri Hamidy Fitaria, Nora Gunawan, Rakhmat Dedi Heni Sulistiani HOTIMAH, NURUL I Gede Heri Susanto Ichtiar Lazuardi Putra Ikbal Yasin Ilham Muhammad Ghoffar Ilham Utama Putra Imam Ahmad Ismail, Izudin Ismail, Izzudin Isnain, Auliya Rahman Jumaryadi, Yuwan 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 Mehta, Abhishek R Meylinda Meylinda Mirza Wijaya Putra Muhammad Bakri Muhammad Fauzan Ramadhani Muhammad Khotimul Anwar Nabil Safiq Ramadan Nirwana Hendrastuty Nova Evrilia Novi Eka Wati Nugraha Ashari Nurhuda Budi Pamungkas Nurhuda Budi Pamungkas Pasaribu, A. Ferico Octaviansyah Prabowo, Rizky Pramita, Galuh Pramuditya, Andri Prastowo, Agung Tri Pratiwi, Eka Shintya Priandika, Adhie Thyo Prita Dellia Purnomo Aji Putra, Ade Dwi Putri Lestari R. Mehta, Abhishek Rachmad Nugroho Rayin Biilmilah Rika Mersita Riska Aryanti Riskiono, Sampurna Dadi Ryan Randy Suryono Saefulloh Saefulloh Salsa Safhira Sampurna Dadi Riskiono Sanriomi Sintaro Saputra, Febrian Eko Sari, Priskila Lovika Setiawansyah Setiawansyah Shely Amalia, Fadila Sitna Hajar Hadad suaidah suaidah Sumanto, Sumanto 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 Wahyudi, Agung Deni Wamiliana Wamiliana Wang, Junhai Waqas Arshad, Muhammad Wayan Kresna Yogi Swara yasin, ikbal Yuri Rahmanto Yusra Fernando Yusri Kusumayuda