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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris WIDYA TEKNIKA Sinergi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science (JOINTECS) Jurnal Ilmiah FIFO JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI J I M P - Jurnal Informatika Merdeka Pasuruan Conference on Innovation and Application of Science and Technology (CIASTECH) JOURNAL OF SCIENCE AND APPLIED ENGINEERING Jurnal Masyarakat Merdeka (JMM) Applied Technology and Computing Science Journal Jurnal Aplikasi Dan Inovasi Ipteks SOLIDITAS Jurnal Pendidikan Manajemen Perkantoran (JPManper) Buana Information Technology and Computer Sciences (BIT and CS) JATI (Jurnal Mahasiswa Teknik Informatika) International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) Aiti: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF) JASEE Journal of Application and Science on Electrical Engineering JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Jurnal Ilmiah Teknik Elektro Prosidia Widya Saintek Jurnal Vokasi Indonesia Journal of Indonesian Tourism and Policy Studies
Istiadi
Program Studi S1 Teknik Informatika Fakultas Teknik, Universitas Widyagama Malang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering 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 Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Social Sciences Transportation Other

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KLASIFIKASI TOPIK SKRIPSI TEKNIK INFORMATIKA BERDASARKAN PEMINATAN MENGGUNAKAN METODE NAIVE BAYES DAN K-NEAREST NEIGHBOR (K-NN) Magai, Etinus; Istiadi, Istiadi; Putra, Rangga Pahlevi
Prosidia Widya Saintek Vol. 4 No. 2 (2025)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pemilihan topik skripsi yang sesuai dengan minat mahasiswa menjadi tantangan bagi program studi Teknik Informatika. Penelitian ini bertujuan mengembangkan sistem klasifikasi topik skripsi berdasarkan peminatan menggunakan algoritma Naive Bayes dan K-Nearest Neighbor (K-NN). Dataset berjumlah 121 judul skripsi diklasifikasikan ke dalam empat kategori: Machine Learning, Data Mining, Web/Mobile, dan IoT/Jaringan. Hasil evaluasi menunjukkan bahwa metode K-NN memberikan akurasi tertinggi sebesar 92%, sedangkan Naive Bayes mencapai 88%. Temuan ini menunjukkan bahwa klasifikasi otomatis berbasis machine learning dapat membantu mahasiswa memilih topik skripsi secara lebih efektif dan sesuai minat.
Klasifikasi Presiden Republik Indonesia Menggunakan SVM Kernel Polynomial Dengan Fitur Ektraksi Gabor Kristianingrum, Kristianingrum; Rahman, Aviv Yuniar; Istiadi, Istiadi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 7: Spesial Issue Seminar Nasional Teknologi dan Rekayasa Informasi (SENTRIN) 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022976748

Abstract

Indonesia adalah negara dengan sistem demokrasi dalam pemerintahannya. Adanya pemilihan presiden yang dilakukan selama 5 tahun sekali dari masa kemerdekaan sampai dengan sekarang. Pemilihan presiden atau yang sering disebut dengan pemilu (pemilihan umum) ini berguna untuk memilih calon presiden dan wakil presiden dalam sebuah negara. Mengingat adanya pergantian presiden setelah 5 tahun dalam 2 periode, para remaja jaman sekarang cenderung mengikuti jaman millennial. Sehingga banyak diantaranya tidak mengenali siapa saja presiden-presiden yang pernah menjabat di Indonesia. Oleh karena itu peneliti mengusulkan Sistem Klasifikasi Presiden Republik Indonesia menggunakan SVM Kernel Polynomial dengan Fitur Ekstraksi Gabor. Tujuan dalam peneliti ini untuk membedakan dan mengklasifikasikan nama presiden berdasarkan dengan foto tersebut. Hasil dalam SVM fitur Gabor kernel Polynomial mendapatkan nilai accuracy tertinggi sebesar 80.77 dengan split ratio 10:90. Parameter precision memiliki nilai tertinggi mencapai 32.56 dengan split ratio 10:90 dan Recall mencapai 32.70 pada split ratio 10:90. Hasil dalam pengujian ini menunjukkan bahwa SVM fitur Gabor kernel Polynomial ialah yang mampu mengklasifikasikan foto presiden dengan baik dan akurat. AbstractIndonesia is a country with a democratic system in its government. Presidential elections are held every 5 years from the time of independence until now. Presidential elections or what are often called elections (general elections) are useful for selecting presidential and vice presidential candidates in a country. considering the change of president after 5 years in 2 periods, today's youth tend to follow the millennial era. So many of them do not know who the presidents who have been in Indonesia are. Therefore, the researcher proposes the Classification System for the President of the Republic of Indonesia using SVM Kernel Polynomial with Gabor Extraction Features. The purpose of this research is to distinguish and classify the name of the president based on the photo. The results in the SVM Gabor Polynomial kernel feature get the highest accuracy value of 80.77 with a split ratio of 10:90. The precision parameter has the highest value reaching 32.56 with a split ratio of 10:90 and Recall reaching 32.70 at a split ratio of 10:90. The results in this test show that SVM features a Gabor Polynomial kernel which is able to classify presidential photos well and accurately.
PAPAYA TYPE CLASSIFICATION USING YOLOv8 Verdiansyah, Egi; Nurdiyansyah, Firman; Istiadi, Istiadi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2336

Abstract

Papaya (Carica papaya L) is a fruit that is easily found in subtropical and tropical regions, including Indonesia. With many varieties of papaya, the manual method used in distinguishing papaya types by humans depends on individual knowledge which can cause inaccuracies in the classification process. The manual classification process also takes a very long time if production is done on a large scale. Therefore, a technology for sorting automation is needed, especially in the industrial world. This research aims to classify papaya classes based on their type. The classification is divided into four classes, namely bangkok papaya, california papaya, hawai papaya, and red lady papaya. The classification process in this study uses the YOLOv8 model, where the total dataset is 1200 papaya images with a training data division of 88% (1050 images), 8% validation data (100 images), and 4% test data (50 images). The dataset is separated according to papaya fruit class. Data training was conducted with 300 epochs. The results show that bangkok papaya has a mAP value of 96%, california papaya 97%, hawai papaya 95%, and red lady papaya has 95% mAP. The average class has a precision value of 99.6%, and recall 100.0%. It can be concluded that the YOLOv8 classification model is able to achieve a high level of accuracy.
Comparison of Machine Learning as an Inference Engine to Improve Expert Systems in Dengue Disease Istiadi, -; Marisa, Fitri; Joegijantoro, Rudy; Suksmawati, Affi Nizar; Rahman, Aviv Yuniar
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3192

Abstract

Dengue disease remains a significant public health challenge in tropical and subtropical regions, with rising incidence and mortality rates over the past few decades. While expert systems have been developed for early detection, traditional approaches often rely on rigid rule-based inference engines, which are limited by their dependence on expert-defined structures and lack adaptability to evolving knowledge sources. This study introduces a novel approach to enhance the flexibility and adaptability of expert systems by integrating machine learning (ML) techniques into the inference engine, leveraging the growing availability of medical record data as a dynamic knowledge source. Using a dataset of 90 medical records, balanced to 126 items via the Synthetic Minority Over-sampling Technique (SMOTE), we evaluated the performance of multiple ML algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), against traditional models like Naive Bayes (NB) and K-Nearest Neighbors (KNN). The DT, SVM, and ANN models demonstrated exceptional performance, achieving average accuracy, precision, recall, and F1 scores of 97.73%, 98.33%, 97.22%, and 97.41%, respectively. The key innovation of this research lies in developing an adaptive inference engine that can dynamically learn from medical data, reducing reliance on static rule bases and enabling the expert system to evolve with new knowledge. This approach improves diagnostic accuracy and provides a scalable and flexible framework for addressing other infectious diseases. By bridging the gap between expert systems and machine learning, this study paves the way for more intelligent, data-driven healthcare solutions with significant implications for public health and disease management.
Strategic Recommendations in Increasing Gen Z User Engagement towards Gamification Elements with Fuzzy AHP and Octalysis Approaches Marisa, Fitri; Istiadi, -; Ahmad, Sharifah Sakinah Syed; Handajani, Endah Tri Esti; NoerTjahyana, Agustinus; Maukar, Anastasia L
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3324

Abstract

Generation Z (Gen Z), often referred to as the "digital native" generation, interacts extensively with digital technology and social media. E-commerce companies need to adopt the right strategies, such as gamification, to increase user engagement among Gen Z. However, there is limited research evaluating which gamification elements are most effective in engaging Gen Z users. This study addresses this gap by identifying the most impactful gamification elements that enhance Gen Z user engagement and providing strategic recommendations for e-commerce designers and developers. Using the Fuzzy AHP method and Octalysis approach, this study evaluates five gamification elements: Point, Reward, Referral, Leaderboard, and Level across four key parameters: Motivation, Engagement, User Experience, and Retention. The Fuzzy AHP results indicate that the "Reward" element ranks highest with a score of 1.0, followed by "Level" with a score of 0.829. "Leaderboard" comes in third with a score of 0.669, while "Point" and "Referral" score 0.606 and 0.220, respectively. The low score of "Referral" suggests its limited effectiveness in fostering social connectedness among Gen Z users. The Octalysis analysis reveals that "Reward" has the most significant influence on core drives such as "Development and Accomplishment" and "Scarcity and Impatience," with an average score of 7.25, followed by "Level" with a score of 7.125. These findings underscore the importance of prioritizing "Reward" and "Level" to optimize user engagement for Gen Z. The practical implications of this study suggest that e-commerce platforms should integrate these gamification elements to create more engaging and interactive shopping experiences for Gen Z users, aligning with their preferences and motivations.
Implementasi GPS Tracker pada Sistem Pemesanan Travel berbasis IoT dengan Metode Adaptive Software Development Prakoso, Anton Bondan; Siswanto, Diky; Istiadi
JASEE Journal of Application and Science on Electrical Engineering Vol. 6 No. 2 (2025): JASEE-September
Publisher : Program Studi Teknik Elektro - Fakultas Teknik - Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jasee.v6i2.04

Abstract

High population mobility requires travel services that are fast, transparent, and able to provide reliable vehicle position information. However, many travel service providers still use manual booking and reporting mechanisms, so customer service, data management, and vehicle monitoring are not yet optimal. This study aims to design and implement a web-based travel booking system integrated with real-time vehicle tracking using Internet of Things technology. The system was developed using the Adaptive Software Development method through the stages of speculation, collaboration, and learning. Vehicle position data were acquired using a Global Positioning System NEO6MV2 module connected to a NodeMCU ESP8266 microcontroller, then transmitted to a server through a web-based application programming interface and displayed on the booking dashboard. System testing showed an average data transmission delay of 1.8 seconds, a data delivery success rate of 98%, and coordinate accuracy within ±3–5 meters. These findings indicate that the proposed system can improve booking data management, accelerate vehicle location monitoring, and increase service transparency for users and administrators. In conclusion, the integration of Internet of Things-based tracking and Adaptive Software Development provides an effective approach for developing adaptive, responsive, and integrated telematics applications for travel service operations.
PENGENDALI ALIRAN AIR BERDASARKAN PEMANTAUAN INTENSITAS UV LAMP STERILIZER PADA PRODUKSI AIR MINUM DALAM KEMASAN BERBASIS IoT Krisdiyanto; Istiadi; Muhsim, Mohammad
JASEE Journal of Application and Science on Electrical Engineering Vol. 6 No. 2 (2025): JASEE-September
Publisher : Program Studi Teknik Elektro - Fakultas Teknik - Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jasee.v6i2.05

Abstract

Bottled drinking water production requires reliable sterilization to ensure microbiological safety after groundwater filtration. Ultraviolet lamps are commonly used as sterilizers; however, decreased light intensity can reduce sterilization effectiveness and affect product quality. This study aims to design and evaluate an Internet of Things-based online monitoring system integrated with Telegram to supervise ultraviolet lamp performance and control product return when the lamp intensity declines. The system was developed using a light-intensity sensor placed at several distances from the ultraviolet lamp, a controller, Telegram notification, and an automatic return valve. Experimental testing was conducted by observing the relationship between sensor distance and received ultraviolet light intensity, followed by regression analysis to determine the warning threshold. The results show that the received light intensity decreases as the sensor moves farther from the ultraviolet lamp, following an exponential trend with a coefficient of determination of 0.8396. When the detected intensity falls below the 75% threshold, the system sends a Telegram warning and activates the return valve to redirect the product to the previous process. Therefore, the proposed system can support real-time monitoring, preventive maintenance, and quality assurance in bottled drinking water production.
Pemanfaatan Kecerdasan Buatan Untuk Mendukung Pengembangan Bahan Pembelajaran Pada SMK Widyagama Malang Istiadi Istiadi; Fitri Marisa; Syahroni Wahyu Iriananda; Rangga Pahlevi Putra
JMM - Jurnal Masyarakat Merdeka Vol. 9 No. 1 (2026): MEI
Publisher : Universitas Merdeka Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51213/jmm.v9i1.217

Abstract

Program pengabdian kepada masyarakat ini bertujuan meningkatkan kompetensi guru SMK Widyagama Malang dalam memanfaatkan kecerdasan buatan, khususnya Generative AI (GenAI), untuk pengembangan bahan ajar vokasional. Metode yang digunakan adalah pendekatan partisipatif melalui observasi, wawancara, workshop interaktif, dan pendampingan selama 2–3 minggu. Pelatihan mencakup penggunaan tools seperti ChatGPT, Canva AI, Diffit, dan MagicSchool. Hasil menunjukkan tingkat kepuasan peserta sebesar 86% serta peningkatan pemahaman dan keterampilan guru dalam mengintegrasikan GenAI ke dalam pembelajaran. Secara keseluruhan, program ini efektif dalam mendorong peningkatan kompetensi digital guru, meskipun diperlukan durasi pelatihan yang lebih panjang untuk hasil yang optimal.
RANCANG BANGUN SMART BOX PENERIMA PAKET EKSPEDISI MENGGUNAKAN WEMOS D1 R2 DAN QR CODE SCANNER Arief Permana Putra; Diky Siswanto; Istiadi
JASEE Journal of Application and Science on Electrical Engineering Vol. 6 No. 2 (2025): JASEE-September
Publisher : Program Studi Teknik Elektro - Fakultas Teknik - Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jasee.v6i2.06

Abstract

Online shopping increases the need for a package receiving system that can operate when the recipient is not at home. This study aims to design and test an Internet of Things-based smart package receiving box using Wemos D1 R2, a QR Code scanner, a fingerprint sensor, solenoid door locks, and a web server. The system allows users to register package receipt numbers through a web interface. Couriers can open the package entry door by scanning a registered receipt code, while users can retrieve the package through fingerprint authentication. System testing was conducted on three main functions: receipt number registration to the database, QR Code-based package access, and fingerprint-based user access. The receipt number registration test showed that all five trials were successfully stored in the database, with a response time range of 2–3 seconds and an average of 2.4 seconds. QR Code validation activated the solenoid lock in all five trials. Fingerprint authentication also succeeded in all five trials under clean finger conditions, with an average door-opening response time of 5 seconds. These results indicate that the proposed prototype can support automatic package reception with controlled access, although wider testing under real delivery and network conditions is still required.
Application of XGBoost Algorithm in Sentiment Classification of MOBA Game Reviews on Google Play Store: Author's Country: Indonesia Daffa Yauzan Tusianto; Syahroni Wahyu Iriananda; Istiadi Istiadi
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 1 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/5x0adm73

Abstract

In the rapidly evolving digital era, business applications like GoBiz play a crucial role in supporting the operations of Micro, Small, and Medium Enterprises (MSMEs). This study aims to analyze user sentiment toward the GoBiz app based on reviews on the Google Play Store by applying two machine learning algorithms: Extreme Gradient Boosting (XGBoost) and Random Forest. Two labeling approaches were used: score-based labeling, which refers to star ratings, and lexicon-based labeling using the VADER method. Data from 10,000 reviews were collected through web scraping and processed through preprocessing, labeling, TF-IDF feature extraction, model training, and evaluation. The evaluation results showed that the XGBoost algorithm excelled in score-based labeling with the highest accuracy of 86.81%, while Random Forest was more stable than the VADER approach with an accuracy of 84.98%. Both models performed well, but their effectiveness depended on the type of labeling used. This research contributes to the development of a sentiment classification system in digital business applications, and can be utilized by GoBiz application developers to improve service quality based on user perceptions.
Co-Authors - Faqih A.A. Ketut Agung Cahyawan W Abd Wahab , Mohd Helmy Adi Saputra, Deni Affi Nizar Suksmawati Affi Nizar Suksmawati Affi Nizar Suksmawati Agatha Korina Intaningtyas Anggarin Anggarin Agustinus Noertjahyana Ahmad Desma Syahputra Ahmad Farhan, Ahmad Ahmad Muzakky Ahmad, Sharifah Sakinah Syed Akbar, Ismail Akhmad Nurhadi Ali Said Alif Dio Raka Wisnu Alif Dio Raka Wisnu Ana Sopanah, Ana Anderias Bai Seran Andriani , Citra Anik Yuesti Antono, Feni Budi April Lia Hananto Ardiansyah Setiawan Arie Restu Wardhani Arie Restu Wardhani Arief Permana Putra Arief Rizki Fadhillah Arofah , Siti Nur Aser Afanti Naa Ashuri Nurdiansyah Aviv Yunia Rahman Aviv Yuniar Rahman Aviv Yuniar Rahman Aviv Yuniar Rahman Aviv Yuniar Rahman Aviv Yuniar Rahman Biaz Surya Prakasa Body Surya Permana Budiawan, Renaldi Widi Candra Zonyfar Daffa Yauzan Tusianto Dean Ariesta Aziz Dedi Usman Effendy Dedi Usman Effendy Dedy Usman Effendi Devita Sari Diky Siswanto Diky Siswanto Dio Amin Putra Dwi Purnomo Dwi Waluyo Putranto Effendi, Dedy Usman Eka Purna Okta Danawan Elko Prayoga Elyana Estyandhika Emma Budi Sulistiarini Eska Riski Naufal Exelino Bata, Jefreydo Fachrudin Hunaini Faqih Faqih Faqih Rofii Fauzi Ahmad Muda Feni Budi Antono Ferry Irmawan Firdaus Iman Ubaidillah Firman Nurdiansyah Firman Nurdiyansyah Firman Nurdiyansyah, Firman Fitri Marisa Fitri Marisa Fitri Marisa Fitri Marisa Fitri Marisa Fitri Marisa Fitri Marisa Fitri Marisa Fitri Marissa Galih Wicaksana Ghafur, A Hanif Saha Grace Januaria Taolin Dirma Hadian Artanto Handajani, Endah Tri Esti Handoko, Rizki Hermawan Suprayogi Hero Diogenes Adoe Ilham Rumaf Ilhamsyah Ilhamsyah Indra Dharma Wijaya Irfan Indra Kurniawan Irfany Rupiwardani Irsandi Satria Wicaksana jauhar, afif Joko Wahyunarto Kartika Yuli Triastuti khusniyatul latifah Kris Inur Firman Sugiarto Krisdiyanto Kristianingrum Kristianingrum, Kristianingrum Kuncahyo Setyo Nugroho Kuncahyo Setyo Nugroho Larangga Herdianzenda Laurentino Da Costa Liliana De Deus Lutfi Erik Prasetyo Magai, Etinus Maheza Fresmanda, Muhammad Mamba’us Sa’adah Maukar, Anastasia L Mochamad Tri Anjasmoros Mochammad Tirta Yovresa Mohammad Yusron Kholish MOHAMMAD YUSUF Monica Putri Indrayati Monica Putri Indrayati Muhamad Mukhsim Muhammad Agus Sahbana Muhammad Ifan Fanani Muhammad Januar Wicaksono Muhsim, Mohammad Mukhsim, Muhamad Mustakim Mustakim Nada Zuhriyah Napulun, Kanisius Naufal, Eska Riski Niken Paramita Pradana, Muhammad Rangga Adi Prakoso, Anton Bondan Priyandoko Gigih Putra Kurniawan, Rizky Putra, Rangga Pahlevi Putra, Sumartono Ali Putranto, Dwi Waluyo Rahma Fitriani Rangga Pahlevi Rangga Pahlevi Putra Rangga Pahlevi Putra Rayana Jaka Surya Raynanda Raynanda Renaldi Widi Budiawan Retno Wilujeng Ridha, Mohammad Riska Suryanti Putri Riska Suryanti Putri Riska Suryanti Putri Rivaldiknas Gampar, Philipus Rizki Handoko Rudy Joegijantoro, Rudy Sabar Setiawidayat, Sabar Sahar, Nan Mad Sandi probo sarjono Sandi Tyas Wahyu Sarina Sulaiman Silviana Silviana Suksmawati, Affi Nizar Syahroni Wahyu Iriananda Syahroni Wahyu Iriananda, Syahroni Wahyu Tjiptoheriyanto, Prijono Triastuti, Kartika Yuli Ubaidillah, Firdaus Iman Udin, M Diya Verdiansyah, Egi Wahyu Iriananda, Syahroni Wicaksono, Padang Widiya Nur Permata Wisnu, Alif Dio Raka Yeni Prasetio Hadi Yuliana Rachmawati Yuninda Wulan Sari Yuninda Wulan Sari