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All Journal Al-Fiziya: Journal of Materials Science, Geophysics, Instrumentation and Theoretical Physics InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Teknologi dan Sistem Komputer JURNAL INTEGRASI QARDHUL HASAN: MEDIA PENGABDIAN KEPADA MASYARAKAT Jurnal Teknologi Terpadu JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat JISA (Jurnal Informatika dan Sains) Indonesian Journal of Electrical Engineering and Computer Science BERDAYA: Jurnal Pendidikan dan Pengabdian Kepada Masyarakat Jurnal Algoritma, Logika dan Komputasi Jurnal Komtekinfo Prosiding University Research Colloquium Telematika MKOM Economic Development Analysis Journal
Yaddarabullah, Yaddarabullah
Universitas Trilogi
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Physics Public Health Social Sciences Other

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Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach Yaddarabullah Yaddarabullah; Aedah Abd Rahman; Amna Saad
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp233-244

Abstract

This study investigates the use of k-nearest neighbors (k-NN) for classifying occupant positions in under-actuated zones, aiming to enhance ventilation control. The focus is on evaluating different data preprocessing techniques, particularly cumulative moving average (CMA), Kalman filtering (KF), and their combination, to boost the k-NN model's reliability and accuracy. The research uses received signal strength indicator (RSSI) data in a controlled setting. The methodology involves dividing the dataset into training and testing subsets and using root mean squared error (RMSE) to determine the best k value for model validation. The study performs a comparative analysis of the k-NN model's performance with both original and preprocessed RSSI data, focusing on metrics such as accuracy, precision, recall, F1-score, and RMSE. The findings emphasize the significant impact of the combined CMA-KF preprocessing technique in improving the model's accuracy and reliability. Specifically, this approach achieved an accuracy of 98.58%. The RMSE values are particularly noteworthy, exhibiting a perfect fit (RMSE of 0) for training data and a remarkably low RMSE of 0.119 for testing data, confirming the model's high accuracy and predictive capability.
Indonesia Cooperative and Members Welfare : a Panel Data Analysis Lestari Agusalim; Muhamad Karim; Yaddarabullah Yaddarabullah
Economics Development Analysis Journal Vol 8 No 1 (2019): Economics Development Analysis Journal
Publisher : Economics Development Department, Universitas Negeri Semarang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edaj.v8i1.26830

Abstract

Indonesia Cooperative is believed as the most appropriate institution in increasing the welfare of its members and the society, and also participating to build the national economic in order to create the advanced society, fair, and prosperous society based on Pancasila and the 1945 Constitution of the Republic of Indonesia. In proving, the purpose of this research is to analyze the effect of Indonesia cooperative existence to the welfare of its members. The method of analysis is the data panel regression, where cross section data covers all provinces in Indonesia, and time series data during 2010-2015. The result shows that the main factors that have effect to the members’ welfare are number of members, number of managers, self-capital, and business volume. The variables that have no significant effect to the members’ welfare are numbers of cooperatives, the annual member meeting (AMM), number of employees, outside capital, and macro economy variable in regional level. Economic growth and outside capital variables have indirect effect to the members’ welfare through volume of cooperative business.
KEGIATAN INKUBASI BISNIS MENGEBANGAN TECHNOPRENEURSHIP DI KALANGAN MAHASISWA UNIVERSITAS TRILOGI Yaddarabullah, Yaddarabullah; Baskoro, Lahandi; Nurulhaq, Bayyinah; Ramayanti, Rizka
Qardhul Hasan: Media Pengabdian kepada Masyarakat Vol. 5 No. 1 (2019): APRIL
Publisher : Universitas Djuanda

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (8528.384 KB) | DOI: 10.30997/qh.v5i1.1292

Abstract

Inkubator Bisnis Trilogi (INBISTRO) adalah unit di Universitas Trilogi yang bertugas untuk mengembangkan kewirausahaan untuk mahasiswa, dosen, almuni dan masyarakat di lingkungan Universitas Trilogi. Tahun 2018 ini, INBISTRO mendapatkan hibah pendanaan dari Kementerian Riset Teknologi dan Pendidikan Tinggi melalui skema Program Pengembangan Kewirausahaan. Hibah tersebut ditujukan untuk melahirkan sejumlah mahasiswa wirausahawan yang mampu menjalankan bisnisnya secara mandiri selepas lulus dari program inkubasi bisnis yang dilakukan oleh INBISTRO. Program tersebut dilakukan dengan proses inti yang terbagi jadi (1) Pra-inkubasi, dimana tenant mendapatkan pengetahuan dasar tentang pengembangan bisnis; (2) Inkubasi, dimana tenant didampingi untuk menyempuranakan produk dan model bisnis; dan (3) Pasca-inkubasi, dimana tenant meluncurkan produk dan dihubungkan dengan mitra strategis. Program inkubasi bisnis yang diselenggarakan oleh INBISTRO membuka kesempatan bagi 20 tim tenant untuk mengembangkan produk dan bisnisnya. Alur proses yang sinergis dari kegiatan perkuliahan dan rangkaian kegiatan inkubasi yang dilakukan oleh Universitas Trilogi bisa dijadikan contoh bagi perguruan tinggi lainnya guna menumbuhkan jumlah wirausahawan teknologi (teknopreneur) berbasis pendidikan tinggi.
RANCANG BANGUN SISTEM INFORMASI PENJUALAN PADA PT. DUTA PERFUME BERBASIS WEB MENGGUNAKAN METODE SEKUENSIAL LINIER Prayuda, Muhammad Fathur; Hermawan, Egie; Aldisetya, Mochammad; Yaddarabullah, Yaddarabullah
Jurnal Algoritma, Logika dan Komputasi Vol 3, No 1 (2020): Jurnal ALU, Maret 2020
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v3i1.2072

Abstract

Permasalahan yang menghambat kemajuan penjualan dapat teratasi karena online marketing tidak mengenal batas wilayah, mempermudah mencari konsumen, promosi, serta transaksi bisnis untuk mengatasi permasalahan dalam penjualan tentang produk-produk yang di hasilkan oleh suatu perusahaan. Dengan adanya online marketing, pengusaha-pengusaha kecil seperti UMKM-K dapat menaikkan nama agar dapat lebih dikenal oleh masyarakat diluar lingkungannya. Sehingga penjualan barang dapat meningkat yang dapat membuaat usaha UMKM-K menjadi lebih maju dan dapat memperluas usahannya. Seperti yang ada pada Toko PT.Duta Perfume yang merupakan perusahaan swasta yang mana bergerak di bidang penjualan perfume atau minyak wangi. Karena itu Toko PT. Duta Perfume memiliki permasalahan bagaimana agar usaha tersebut bisa diperluas dalam mendapatkan customer baik di tempat tersebut maupun di daerah lain untuk membeli produk tersebut. Untuk mengatasi masalah tersebut Toko PT. Duta Perfume ingin membuat serta membangun sebuah sistem informasi yang lebih tepatnya bergerak dibidang online. Pada penelitian ini akan membangun sistem informasi berbasis web menggunkan metode sekuensial linear, proses implementasi dilakukan dengan menggunakkan visual studio code sebagai text editor, menggunakan bahasa program PHP dan pengorganisasian RDBMS menggunakan MySQL. Proses pengujiannya menggunakan metode black-box testing. Hasil dari penelitian ini dapat membantu penjualan PT. Duta Perfume dalam memanfaatkan online marketing (pemasaran online) dengan menggunakan teknologi e-commerce.
Keamanan Sistem Login Menggunakan Multifactor Authentication dan Algoritma Hashing Fitriyani, Aida; Lubis, Hendarman; Yaddarabullah, Yaddarabullah; Sari, Rafika
Journal of Information System, Informatics and Computing Vol 9 No 2 (2025): JISICOM (December 2025)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v9i2.2137

Abstract

Login system security is a crucial aspect in digital application development to protect user personal data. This research aims to implement a modern hashing algorithm, Argon2, and add a security layer through Multi-Factor Authentication (MFA) using One-Time Password (OTP) codes. The Argon2 algorithm was chosen because it is memory-hard, configurable, and more resistant to brute-force attacks and rainbow table attacks. The system development method used is the Waterfall method, where the system is developed using the Kotlin programming language with the Jetpack Compose architecture, and the Firebase Authentication and Firestore databases. An OTP is sent to the user after successful registration as additional verification. Implementation results show that the time cost parameter in Argon2id significantly affects hashing execution time, with higher values improving security while remaining within performance tolerances. The combination of Argon2 and MFA successfully provides a more secure login system and is more resilient to cyberattacks than traditional authentication methods. Test results indicate that the Time Cost parameter in Argon2id significantly impacts hashing time. With the following configurations: Time Cost = 1 → 2.028 ms, Time Cost = 2 → 48.623 ms, and Time Cost = 3 → 71.219 ms. Higher parameter values increase execution time but also increase resistance to brute-force attacks. The combination of the Argon2 algorithm and MFA methods results in a more secure and reliable login system than traditional authentication, while remaining responsive in application performance.
Pengembangan Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik Menggunakan Metode AHP pada Evaluasi Kinerja 360 Degree Feedback Muhammad Sulthan Rafli Maajid; Silvester Dian Handy Permana; Yaddarabullah Yaddarabullah
Jurnal Teknologi Terpadu Vol 11 No 2 (2025): Desember, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v11i2.2532

Abstract

PT. X is a health technology company that provides a cloud-based Management Information System for healthcare facilities. So far, the selection of the best employees has been based solely on supervisors' observations, without a systematic, objective evaluation system. This has led to decreased employee performance and motivation due to the lack of a structured, objective decision-making system. This study aims to develop a performance evaluation system based on the Analytical Hierarchy Process (AHP) method, integrated with the 360 Degree Feedback approach, to provide an objective, structured assessment. The evaluation is conducted based on clear, measurable criteria such as professionalism, performance, attitude, initiative, and teamwork, involving assessments from supervisors, colleagues, subordinates, and self-evaluation, thereby ensuring a more comprehensive assessment and reducing the bias commonly found in single-source evaluations. The results of the study show that the best employees based on this system are Samuel Steven (1.00), Muhammad Sulthan Rafli Maajid (0.947675), and Muhammad Insan Kamil (0.920534). This system is expected to improve evaluation quality and employee motivation by enabling more comprehensive, objective assessments.
Implementation Internet of Things for Feeding Catfish Water Quality Analysis Using Linear Regression and K-Nearest Neighbor Yaddarabullah, Yaddarabullah; Hermawan, Egie
JISA(Jurnal Informatika dan Sains) Vol 5, No 2 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i2.1431

Abstract

Cultivation of catfish (Clarias Gariepinus) is a promising business field and also a very productive activity because public interest in catfish is high. This factor is observed by market demand for catfish which is increasing from year to year. In catfish farming, you must pay attention to the acidity of the water (pH), temperature, and oxygen levels, which can change if too much feed is given. This can cause catfish seedlings to die and affect the catfish harvest. Catfish farmers often provide excessive food which causes many catfish seeds to die. This research will conduct a study on an Internet Of Things technology that can be used to monitor the acidity level in water pH, temperature, and oxygen levels as well as feed fish. The Internet of Things is very influential for monitoring the quality of catfish ponds by distributing information data resulting from sensor monitoring. The data obtained will be predicted for water quality in the pond by implementing a Linear Regression method. Furthermore, the acquisition of data from the predictions that have been carried out will be processed again to go to the next phase, namely classifying with the K-Nearest Neighbor algorithm method to carry out the identification phase of water types based on the nearest neighbors. This prediction is used to anticipate and notify catfish farmers through applications if there is a water acidity level (pH), temperature, and oxygen and feed levels that have run out
Implementation of TF-IDF Algorithm and K-mean Clustering Method to Predict Words or Topics on Twitter Darwis, Muhammad; Pranoto, Gatot Tri; Wicaksana, Yusuf Eka; Yaddarabullah, Yaddarabullah
JISA(Jurnal Informatika dan Sains) Vol 3, No 2 (2020): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v3i2.831

Abstract

The social media time line, especially Twitter, is still interesting to follow. Various tweets delivered by the public are very informative and varied. This information should be able to be used further by utilizing the topic of conversation trends at one time. In this paper, the authors cluster the tweet data with the TF-IDF algorithm and the K-Mean method using the python programming language. The results of the tweet data clustering show predictions or possible topics of conversation that are being widely discussed by netizens. Finally, the data can be used to make decisions that utilize community sentiment towards an event through social media like Twitter.  
Co-Authors Aedah Abd Rahman Aida Fitriyani, Aida Aldisetya, Mochammad Amna Saad Arifitama, Budi Baskoro, Lahandi Dewi Lestari Doni Kurniawan Galih Suprayitno Gatot Tri Pranoto Hermawan, Egie Hermawan, Egie Karim, Muhamad Karim, Muhamad Krishnasari, Erneza Dewi Lestari Agusalim Lubis, Hendarman Muh. Darwis Muhamad Karim Muhammad Sulthan Rafli Maajid Nazori AZ Nurulhaq, Bayyinah Prayuda, Muhammad Fathur Rafika Sari Ramayanti, Rizka Randy Teguh Waluya Nugraha Silvester Dian Handy Permana, Silvester Dian Handy Wicaksana, Yusuf Eka