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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Scientific Journal of Informatics Jurnal Inspiration KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization Journal of Information Technology and Computer Science INTEGER: Journal of Information Technology JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Prosiding Seminar Nasional Sains dan Teknologi Terapan Jurnal Mantik Jurnal Teknologi Informasi dan Terapan (J-TIT) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah Teknologi Informasi dan Robotika Jurnal Teknologi dan Manajemen KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) West Science Social and Humanities Studies NERO (Networking Engineering Research Operation)
Gusti Eka Yuliastuti
Institut Teknologi Adhi Tama Surabaya
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Prediction Of Clay Mining Production Value Using Linear Regression Model With Multi-Swarm Particle Swarm Optimization Yuliastuti, Gusti Eka; Kurniawan, Muchamad; Pratikto, Dimas; Moneter, Mochamad Rizky
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.3443

Abstract

The progress of a nation or a country can be recognized from its income through various industries inside. Mining refers to one of the most advanced industries in Indonesia. The majority of mining in Indonesia is open-pit mining which is exposed directly to the sky. This study focuses on modeling data from rainfall, working hours, and production yields. It employed the Multi-Swarm Particle Swarm Optimization (MSPSO) algorithm to find multiple linear regression modeling by minimizing the Mean Squared Error (MSE) value. The value for the production results was then predicted using the existing multiple linear regression model. In terms of testing, the best model having an MSE of 288.0656 occurred at the parameters of Npop 180, acceleration coefficient 1 by 0.7, acceleration coefficient 2 by 0.7, acceleration coefficient 3 by 0.7, wmin 4, wmax 9 within 100 iterations.
Rancang Bangun Pemodelan Jaringan Hotspot Menggunakan Mikrotik Pada CV Ahza Computer Dengan Hierarchical Token Bucket (HTB) Ardiansah, Ferdi; Nurmuslimah, S; Yuliastuti, Gusti Eka
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7609

Abstract

Pada saat ini banyak orang menggunakan internet sehingga menjadi kebutuhan wajib diera sekarang ini. Namun, dengan berkembangnya interenet terdapat masalah yaitu manajemen bandwith dan pemanfaatan Quality of service yang belum maksimal. Tanpa adanya manajemen bandwith maka akan menimbulkan terjadinya masalah pada bandwith yang diterima client maupun user.Penelitian ini bertujuan untuk mengetahui bagaimana penerapan pembagian bandwith menggunakan Hierarchical token bucket pada layanan hotspot Cv. Ahza Computer, dan untuk mengetahui cara mengolah data hotspot menggunakan mikrotik.Metode penelitian yang digunakan adalah research and development, dengan melalui tahapan pengembangan, pendefinisian, desain, penyebaran, manajemen bandwith. Hasil pengujian skala likert atau kuesioner didapatkan nilai rata-rata 92% untuk kecepatan internet yang merata disetiap client dan user. Selama dilakuakn pengujian internet didapatkan hasil throughput 2910 Kbits/s, Packet loss 0%, Delay 2,638ms, Jitter 2,78 ms. Pengujian dilakukan menggunakan aplikasi wireshark dan disimpulkan bahwa dengan hierarchical token bucket dapat membagi bandwith secara adil dan merata secara hirarki sesuai divisi yang ada di Cv. Ahza Computer. Keywords : Hotspot mikrotik, HTB, Quality Of Service, Manajemen Bnadwith
Literature Review on Public Policy and Social Impact in Drug Rehabilitation in Indonesia Ngaisah, Siti; Haryono, Haryono; Yuliastuti, Gusti Eka
West Science Social and Humanities Studies Vol. 3 No. 08 (2025): West Science Social and Humanities Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsshs.v3i08.2186

Abstract

This study examines the convergence of public policy and social impact on drug rehabilitation for addiction in Indonesia using a literature review of 13 Scopus-indexed articles. Findings highlight that Indonesia has evolved in its drug policies from punitive measures towards incorporating rehabilitative interventions, although there continue to be prevalent challenges. These include stigma, poor infrastructure, uneven policy application, and lack of inter-stakeholder coordination. Models of rehabilitation both community and faith-based also have the potential for strengthening social reintegration and reducing recidivism. The research demands policy alignment, increased investment, and stronger multi-stakeholder coordination to achieve effective and inclusive rehabilitation. Evidence emerging from this research provides a foundation for evidence-based policy and program development particular to Indonesia's socio-culture.
Application of IoT-based Intelligent Control Devices Empowered with Fuzzy Inference System in the Garment Industry Rizki, Agung Mustika; Ashari, Faisal; Yuliastuti, Gusti Eka; Haromainy, Muhammad Muharrom Al; Aditiawan, Firza Prima; Amnur, Hidra
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3344

Abstract

The garment industry in Indonesia has experienced significant development in recent years. A critical aspect of this development is the increasing role of Micro, Small, and Medium Enterprises (MSMEs). Swari Garment Industries (SGI) is an example of an MSME that focuses on the garment sector. In practice, various problems and negligence can affect the course of the production process. One potential issue is using the machine inappropriately or excessively, which can lead to a short electrical circuit. Short electrical circuits are one of the problems that must be faced because they can cause various severe impacts, including equipment damage and even fire. Based on this risk analysis, a possible solution to be applied to SGI, one of the MSMEs in the garment sector, is the implementation of an intelligent control device. The implementation of intelligent control tools based on the Internet of Things (IoT) can enhance the efficiency of the production process and mitigate significant risks to workers and the environment. The Fuzzy Inference System, in which the equity, temperature, and humidity are the input values of the Intelligent Control Device. A hardware device for temperature and humidity control, accessible through an Android phone application, was implemented in SGI. Experiments have verified that we can achieve excellent results. The average percentage of temperature measurement error was 0.2% and for humidity, 0.26%. The average percentage of measurement error from the comparison between the system and MATLAB is 0.49%.
Klasifikasi Jenis Jerawat pada Data Citra Jerawat Wajah Menggunakan Convolutional Neural Network Putri, Chatarina Natassya; Qornain, Wafi Dzul; Bamahri, Fakhirah; Yuliastuti, Gusti Eka; Kurniawan, Muchamad
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5231

Abstract

Acne is a condition caused by pilosebaceous inflammation which affects 85% of skin conditions in adolescents and adults. Acne has an impact on the psychological and social health of sufferers. To treat acne, it is necessary to know the right type of acne so that sufferers can treat the type of acne according to how they are treated. This research was carried out to classify the types of acne in facial acne images using the Convolutional Neural Network (CNN) method. Based on previous research, it shows that the use of CNN is considered effective and appropriate in increasing classification accuracy. This research uses a dataset of acne types from Kaggle with a total of 351 data, divided into 5 classes, namely acne fulminans, acne nodules, fungal acne, papules and pustules which will be tested using 2 different optimizers, namely Adam and RMS- prop. From the results of this test, the highest accuracy was 100% using the Adam optimizer and the RMS-prop optimizer test obtained the highest accuracy value of 80%.
Implementing K-Nearest Neighbors (k-NN) Algorithm and Backward Elimination on Cardiotocography Datasets Kurniawan, Muchamad; Yuliastuti, Gusti Eka; Rachman, Andy; Budi, Adib Pakar; Zaqiyah, Hafida Nur
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1996

Abstract

Having a healthy baby is a dream for mothers. Unfortunately, high maternal and fetal mortality has become a vital problem that requires early risk detection for pregnant women. A cardiotocograph examination is necessary to maintain maternal and fetal health. One method that can solve this problem is classification. This research analyzes the optimal use of k values and distance measurements in the k-NN method. This research expects to become the primary reference for other studies examining the same dataset or developing k-NN. A selection feature is needed to optimize the classification method, particularly for improving accuracy results. This study used the cardiotocography dataset from cardiotocograph examinations related to fetal conditions. The cardiotocography dataset consisted of 2,126 records with 22 features and variables. It also had three classification classes, normal, suspect, and pathological, from the Universal Child Immunization Machine Learning Repository website. It employed the K-Nearest Neighbor (k-NN) method and the backward elimination feature with ordinary least squares regression. The test in this research applied the scenarios of three distance calculations, i.e., Euclidean distance, Manhattan distance, and Minkowski distance, as well as four variations of k values. Evaluation of each scenario indicated the accuracy of the confusion matrix and execution time. This study compared K-Nearest Neighbor (k-NN) and Backward Elimination methods with K-nearest neighbor (k-NN) without selection features. The best accuracy of the Backward Elimination and K-Nearest Neighbor (K-NN) methods was 91%, as was the K-Nearest Neighbor (k-NN) method without selection features. Both had similar k values (k = 3) and Manhattan distance. The backward elimination method reduced the number of features from 22 to 14. Meanwhile, the execution times of the Backward Elimination and K-Nearest Neighbor (k-NN) methods got better results as each distance averaged 26.54, 19.23, and 68.09 seconds. K-Nearest Neighbor (k-NN) execution times without selection features were 26.83, 19.39, and 68.84, respectively. In conclusion, backward elimination did not increase accuracy because it yielded the same accuracy. However, backward elimination and K-nearest Neighbor (k-NN) produced faster results, with differences of 29%, 16%, and 75%, respectively.
Co-Authors Achmad Basyari Mushthofa Achmad Febrianto Adjie Prasetyo Nugroho Agung Mustika Rizki Agung Mustika Rizki Agung Mustika Rizki Agung Mustika Rizki, Agung Mustika Alphinda Rahma Safira Nisa' Andi M. A. K. Parewe Andy Rachman Anwar Sodik, Anwar Ardiansah, Ferdi Ariansyah, Fernando Yoga ASHARI, FAISAL Assyarif, Rafli Abi Azizi, Ilham Habib Bamahri, Fakhirah Budi, Adib Pakar Citra Nurina Prabiantissa Danang Haryo Sulaksono Danang Haryo Sulaksono, Danang Haryo Dhian Satria Yudha Kartika Dhiyas Rakha Allam Allam dwi, Igusti Ngurah Eka Prakarsa Mandyartha Faisal Muttaqin Fery Soewarianto Firmansyah, Mohammad Hafitz Firmansyah, Muhammad Farid Firza Prima Aditiawan Habibullah, Maulana Ahmad Haryono Haryono Hendro Nugroho Hendro Nugroho, Hendro Hidra Amnur I Kadek Agus Ariyasa Ishardita Pambudi Tama Ismaya, Agam Sulaiman Jaya, Agam Kaisul Fuqara Dewanda Khoiri, Muhammad Kusuma, Ris Fani M. Fajar J. Kharisma Maulana, Fadhil Dias Maulana, Hendra Moneter, Mochamad Rizky Muchamad Kurniawan, Muchamad Muhammad Muharrom Al Haromainy Muhammad Rohman Muhammad Zazin Nanang Fakhrur Rozi Ngaisah, Siti Nurlaili, Afina Lina Oktavyani, Adela Rizky Pamuji, Madadina Adilah Pradana, Andrean Firman Pratikto, Dimas Putra, Erico Maulana Putri, Chatarina Natassya Putri, Rahmi Rizkiana Qornain, Wafi Dzul Radissa Dzaky Issafira Rahmadianto, Ilyasa Nanda Rahmi Rizkiana Putri S Nurmuslimah, S Septiyawan Rosetya Wardhana Setiawan, Muhammad Charis Siti Agustini Siti Agustini, Siti Soedarto, Teguh Suthelie, Ryvana Teguh Soedarto Wardhana, Septiyawan Rosetya Wayan Firdaus Mahmudy Weny Mistarika Rahmawati Wicaksono, Redi Nurdin Yunanda, Sita Fara Zaqiyah, Hafida Nur