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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Dinamik Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JURNAL SISTEM INFORMASI BISNIS Jurnal Sistem Komputer JSI: Jurnal Sistem Informasi (E-Journal) Prosiding SNATIF Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Desimal: Jurnal Matematika INOVTEK Polbeng - Seri Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan International Journal on Emerging Mathematics Education Jurnal ULTIMA InfoSys MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Sistem Informasi dan Aplikasi Journal of Information Technology and Computer Engineering J-SAKTI (Jurnal Sains Komputer dan Informatika) Aptisi Transactions on Management JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Aptisi Transactions on Technopreneurship (ATT) EDUKATIF : JURNAL ILMU PENDIDIKAN Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Journal of Information Systems and Informatics KAIBON ABHINAYA : JURNAL PENGABDIAN MASYARAKAT Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) ICIT (Innovative Creative and Information Technology) Journal Computer Science and Information Technologies Jurnal Bumigora Information Technology (BITe) Aiti: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF) IAIC Transactions on Sustainable Digital Innovation (ITSDI) JOINTER : Journal of Informatics Engineering International Journal of Engineering, Science and Information Technology Advance Sustainable Science, Engineering and Technology (ASSET) Journal of Information Technology (JIfoTech) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat JEECS (Journal of Electrical Engineering and Computer Sciences) Metris: Jurnal Sains dan Teknologi Scientific Journal of Informatics International Journal of Information Technology and Business INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal DIMASTIK
Hindriyanto Dwi Purnomo
Universitas Kristen Satya Wacana
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Prediksi Kegagalan Transformator Daya dengan Metode DGA (Dissolved Gas Analysis) Menggunakan Random Forest Berbasis TDCG Sugiman, Marcelino Maxwell; Purnomo, Hindriyanto Dwi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7036

Abstract

Transformers are critical components, and early detection of potential failures plays an important role in the reliable operation of the power system. This article describes a novel approach for power transformer failure prediction based on dissolved gas analysis (DGA) by applying the TDCG method with Random Forest algorithm. DGA data from operational transformers are used to train and test the predictive model. The Random Forest method based on TDCG enables comprehensive analysis of dissolved gas changes in transformer oil, thus enabling early detection of failure conditions. Experimental results show that the predictive model using the model created by applying hyperparameter tuning for optimal parameter tuning to have high accuracy, the accuracy obtained reaches 96% in detecting potential failures, the standard used for accuracy presentation uses confusion matrix as the accuracy of the predictive model. In addition, it can optimize time efficiency in analyzing failures and prevent human error when calculating gas fault identification or potential failures.
Pengelompokan Pemenang Tender dengan Metode K-Means Clustering (Kasus Layanan Pengadaan Secara Elektronik Bagian Pengadaan Barang/Jasa Kabupaten Semarang) Utama, Deffa Ferdian Alif; Purnomo, Hindriyanto Dwi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 8, No 1 (2024): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v8i1.765

Abstract

Electronic Procurement Services (LPSE) is a work unit formed specifically to serve the Goods/Services Procurement Work Unit (UKPBJ), responsible for carrying out the procurement process electronically. currently, the method of selecting providers has become e-regular tendering which is all done online, the evolution of electronic tendering systems has grown rapidly along with advances in information technology. This research uses the data of tender winners that have been done Data Mining before from LPSE Semarang Regency, from 2011 to 2022 with the population of completed tenders, which aims to group the data of tender winners to find out what procurements have been completed and the role of local entrepreneurs / companies. total of 2127, using only 238 random data samples referring to the method of Isaac and Michael. using the method of Machine Learning, namely the Unsupervised Learning method processed with the K-Means Clustering Algorithm, the results obtained are the Construction of Public Facilities in the form of types of Construction Work, such as roads, tourist attractions, facilities for villages and others both made new, upgrades and others. then comes from the Semarang Regency area by showing 8 out of 13 or two-thirds with a percentage of 62%. This information is useful as an overview, education, information, knowledge for all of us in the Semarang Regency area and all the people of Indonesia as well as helping the economic development of the region and all other regional areas to the National.
Assessing Employee Performance in the Information Technology Department Using K-Means Clustering: A Case Study Approach Sakalessy, Afelia Jozalin Elisa; Purnomo, Hindriyanto Dwi
Journal of Information System and Informatics Vol 6 No 1 (2024): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i1.653

Abstract

This research explores the utilization of the K-Means method for evaluating the performance of employees within the Information Technology Department at PT Nusa Halmahera Minerals. The research leverages data from Manage Engine to analyze diverse parameters of employee performance. By employing the K-Means method, employees are categorized based on specific characteristics, facilitating a deeper understanding of each individual's contribution and potential in attaining company objectives. The implementation of the K-Means method aims to offer a more objective perspective on employee performance, empowering companies to make informed decisions in the enhancement and development of their human resources.
Analyzing the Distribution of Health Workers in Semarang City Using K-Means Clustering Method Setiyaji, Akhfan; Purnomo, Hindriyanto Dwi
Journal of Information System and Informatics Vol 6 No 1 (2024): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i1.663

Abstract

This research employed the K-Means Clustering method to examine the distribution of health workers in Semarang City, emphasizing their pivotal role in the public health infrastructure. Leveraging current data encompassing health worker locations and quantities, the clustering analysis discerned areas exhibiting similar distribution characteristics through the application of the K-Means technique. Quantitative analysis revealed distinct clusters, shedding light on the spatial patterns of health workforce dispersion within Semarang City. The study's quantitative findings furnish valuable insights crucial for formulating more efficacious health policies. By delineating the utility of the K-Means Clustering method in public health planning and providing quantitative evidence of health worker distribution, this research substantially augments geographical comprehension in the examined region.
Air Quality Prediction Using the Support Vector Machine Algorithm Widyarini, Liza; Purnomo, Hindriyanto Dwi
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.705

Abstract

Air quality is an important factor in maintaining the health and well-being of humans and the environment. To anticipate and manage air pollution, air quality prediction has become an important research topic. In this research, researchers propose using the Support Vector Machine (SVM) algorithm to predict air quality. SVM has proven to be an effective method in classification and regression, especially in the context of complex and non-linear data such as air quality data. Researchers utilized historical air quality datasets that include various parameters such as particulates, ozone, nitrogen dioxide and carbon monoxide. Experiments were conducted to compare the performance of SVM with other prediction methods, and the results show that SVM provides accurate and reliable predictions in modeling air quality.
Progress in Non-Invasive Cognitive Brain-Computer Interface and Implications for Mind-Uploading Astawa, I Wayan Aswin Dew; Purnomo, Hindriyanto Dwi; Sembiring, Irwan
International Journal of Artificial Intelligence Research Vol 8, No 1 (2024): June 2024
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1133

Abstract

Mind-uploading, the vision of transferring human consciousness into a digital realm, relies on a profound comprehension of the brain and cutting-edge technology. Non-invasive cognitive Brain-Computer Interfaces (BCI) offer a promising avenue for delving into neural activity and bridging the brain-machine gap. This research explores the potential of non-invasive cognitive BCI in realizing mind-uploading through a systematic literature review (SLR), analyzing recent research that focuses on its current progress and implications for mind-uploading. The SLR unveils significant strides in non-invasive cognitive BCI, demonstrating increased precision in recording and decoding cognitive processes and fostering a deeper understanding of these processes. This progress is attributed to a diverse range of emerging feature extraction and decoding methods, transforming subtle neural signals into interpretable commands. Notably, advancements in signal processing and neuroimaging techniques enhance communication speed and clarity between the brain and computer. Furthermore, the development of cost-effective methods, frameworks, and hardware holds the promise of broader accessibility to BCI technology. However, significant hurdles remain. The computational demands of current cognitive BCI systems pose a substantial challenge, while the scarcity of high-quality training datasets hampers algorithm development and accuracy. The poor signal quality causes difficulties in recording neural complexity and hampers accuracy. In conclusion, non-invasive cognitive BCI has significant potential to pave the way for mind-uploading. However, its limitations, make their capabilities remain insufficient to fully realize this ambitious vision. This highlights the critical need for sustained research and innovation to bridge the gap between current understanding and the exciting realm of mind-uploading.
Optimalisasi Hyper Parameter Convolutional Neural Networks Menggunakan Ant Colony Optimization Santoso, Fian Yulio; Sediyono, Eko; Purnomo, Hindriyanto Dwi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 2: April 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241127105

Abstract

Berbagai bidang, termasuk pertanian dan kesehatan, mengalami masalah klasifikasi citra yang dapat diatasi melalui beberapa metode. Salah satu metode tersebut menggabungkan convolutional neural networks (CNN) dengan deep learning, tetapi hyperparameter, seperti fungsi loss, fungsi aktivasi, dan optimizers, memengaruhi kinerjanya. Hyperparameter ini memerlukan pengoptimalan, dan metode yang ada, seperti algoritma genetika dan pengoptimalan ant colony, dapat digunakan untuk tujuan ini. Pengoptimalan ant colony terbukti efektif dalam mengoptimalkan deep learning, dan penelitian ini berkontribusi pada penyetelan otomatis berbagai hyperparameter menggunakan ant colony untuk klasifikasi gambar. Pada penelitian ini menggunakan dataset MNIST yang bertujuan untuk mengidentifikasi digit pada citra. Dataset yang digunakan terbagi menjadi 2, dataset pelatihan dan dataset validasi. Dataset pelatihan terdiri dari 33.600 gambar, dan dataset validasi terdiri dari 8.400 gambar. Hasil menunjukkan bahwa optimasi ant colony mencapai akurasi 97,46% dengan data validasi dan 99,69% dengan data pelatihan, yang mengungguli algoritma genetika dengan akurasi masing-masing 94,60% dan 97,59% dengan data validasi dan pelatihan. Selain itu, pengoptimalan ant colony membutuhkan waktu 27,94 detik untuk dilatih, sedangkan algoritme genetika membutuhkan 22,25 detik.
Analisis Sentimen E-Learning X Terhadap Antarmuka Pengguna Menggunakan Kombinasi Multinomial Naive Bayes Dan Pendekatan Design Thinking Huda, Baenil; Sembiring, Irwan; Setiawan, Iwan; Manongga, Danny; Purnomo, Hindriyanto Dwi; Hendry, Hendry; Fauzi, Ahmad; Lia Hananto, April; Tukino, Tukino
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 4: Agustus 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.1147686

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap antarmuka e-learning X menggunakan kombinasi Multinomial Naive Bayes dan pendekatan Design Thinking. Permasalahan yang dihadapi adalah banyaknya feedback negatif terkait antarmuka pengguna yang dianggap kurang intuitif. Data sentimen dari ulasan pengguna diklasifikasikan menggunakan algoritma Multinomial Naive Bayes, sementara Design Thinking digunakan untuk merancang solusi antarmuka yang lebih user-friendly. Hasilnya menunjukkan bahwa metode ini efektif meningkatkan sentimen positif pengguna, dengan perbaikan signifikan dalam pengalaman dan kepuasan pengguna terhadap antarmuka e-learning X, Serta rekomendasi untuk pengembangan aplikasi e-learning.   Abstract   This research aims to analyze user sentiment towards the e-learning interface X using a combination of Multinomial Naive Bayes and Design Thinking approaches. The problem faced was the large number of negative feedback regarding the user interface which was considered less intuitive. Sentiment data from user reviews is classified using the Multinomial Naive Bayes algorithm, while Design Thinking is used to design more user-friendly interface solutions. The results show that this method is effective in increasing positive user sentiment, with significant improvements in user experience and satisfaction with the X e-learning interface As well as recommendations for developing e-learning applications.
Pengembangan Perangkat Lunak Berbasis Website Menggunakan Kombinasi Metode Scrum Dan V-Model Tumbade, Marcho Oknivan; Hartomo, Kristoko Dwi; Purnomo, Hindriyanto Dwi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 3: Juni 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.938456

Abstract

Penelitian membahas software engineering berbasis website dengan hybrid model integrasi pada universitas terkait. Rumusan masalah penelitian menghasilkan software engineering berbasis website mudah digunakan serta diakses mahasiwa berstatus aktif, menghasilkan software engineering layanan berbasis website mampu melacak status aduan, dan efektivitas serta efisiensi hybrid model integrasi antara metode Agile Scrum dan V-Model dalam development software engineering. Hasil penelitian untuk menyelesaikan masalah menggunakan metode deskriptif kualitatif dengan dukungan hybrid model integrasi Scrum dan V-model sehingga mengusulkan integrasi Scrum V-Plus berbasis website, hybrid model integrasi memanfaatkan kelemahan masing masing metode agar saling melengkapi, sehingga peranan Scrum sebagai tahapan awal perancangan lalu dikembangkan V-model secara terstruktur dan tersistematik kedalam modul development proses software engineering. Dengan adanya Model Integrasi Scrum V-Plus yang berjalan maka dilakukan rancangan prototype model, design dan pengujian website sebagai media validasi pembuktian bahwa dokumentasi proses bisnis secara terstruktur memiliki dampak signifikan terhadap kolaborasi metode dalam menghasilkan produk. Penelitian mengusulkan Model Scrum V-Plus Hybrid yang diadopsi dari kolaborasi kedua metode untuk berperan penting terhadap tahapan perancangan engineering terstruktur, fleksibel secara keseluruhan dalam mengintegrasikan beberapa tahapan, sehingga rancangan software terlaksana dengan baik dibuktikan model integrasi website bagi mahasiswa berstatus aktif menggunakan open data base connectivity restfulAPI pada deployment diagram berdasarkan model class diagram yang dilengkapi dengan boundary, entity dan conrol. Hybrid model integrasi memiliki dampak signifikan dibandingkan penggunaan secara terpisah dengan dibuktikan integrasi testing.
Leveraging Machine Learning Models to Enhance Startup Collaboration and Drive Technopreneurship Wijono, Sutarto; Rahardja, Untung; Purnomo, Hindriyanto Dwi; Lutfiani, Ninda; Yusuf, Natasya Aprila
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 3 (2024): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i3.462

Abstract

In the dynamic and competitive realm of startups, identifying and cultivating effective collaborations is crucial for sustained success. This research evaluates how machine learning (ML) technologies can enhance startup collaborations by advancing decision-making processes through the analysis of historical data. Employing the SmartPLS methodology, this study collected data from 220 stakeholders, including 207 actively engaged in startups that are either utilizing or integrating ML technologies. The investigation focuses on understanding ML models, the importance of historical data, and the dimensions of collaboration critical to the success of startups. Through analysis with PLS-SEM, it was found that ML models significantly boost inter-startup synergy and the effectiveness of collaborative efforts. The results provide vital insights for industry practitioners and strategic decision-makers, offering practical strategies to employ ML in optimizing collaboration and ensuring sustainable growth within the technopreneurship arena. This study not only highlights the benefits of ML in fostering cooperative ventures but also aims to refine the strategic frameworks essential to the startup ecosystem.
Co-Authors 12.5202.0161 Daniel Yeri Kristiyanto Ade Iriani Adimas Tristan Nagara Hartono Adriyanto Juliastomo Gundo Agung Wibowo Agus Priyadi Ahmad Bayu Yadila Andre Kurniawan Andrew Aquila Chrisanto Pabendon Andry Ananda Putra Tanggu Mara Andry Tanggu Mara Angela Atik Setiyanti Ani, Nyree Anton Hermawan Anwar, Muchamad Taufiq April Firman Daru April Lia Hananto Aris Puji Widodo Arseta, Gama Astawa, I Wayan Aswin Dew Atik Setyanti, Angela Aziz Jihadian Barid Azzahra Nurwanda Bandung Pernama Baun, Sindy Cristine Budhi Kristianto Budi Kristianto Budi Kristianto, Budi C. Leuwol, Sylvie Cahyaningtyas, Christyan Cahyo Dimas K Cesna, Galih Putra Chandra Halim Charitas Fibriani Christyan Cahyaningtyas Daniel Kurniawan Daniel Kurniawan Danny Manongga Danu Satria Wiratama Deden Rustiana Dedy Prasetya Kristiadi Didit Budi Nugroho Dody Agung Saputro Dwi Hosanna Bangkalang Edwin Zusrony Eko Sediyono Eliansion Ivan eremia Silvester Sutoyo Erwien Christianto Evang Mailoa Evangs Mailoa Fajar Rahmat Faudisyah, Alfendio Alif Fauzi Ahmad Muda Feibe Lawalata Florentina Tatrin Kurniati Giner Maslebu Gladis Tri Enggiel Griya Jitri Pabutungan Gudiato, Candra Hanita Yulia Hanna Arini Parhusip Hari Purwanto Hendra Kusumah Hendra Waskita Hendradito Dwi Aprillian Hendro Steven Tampake Hendry Heni Pujiastuti Hermanto Abraham, Rendy Hery Santono HR. Wibi Bagas N Hsin Rau Huda, Baenil Hui-Ming Wee Irdha Yunianto Irwan Sembiring Istiarsi Saptuti Sri Kawuryan Istiarsih Saputri Sri Kawuryan Iwan Setiawan Iwan Setyawan Janinda Puspita Anidya Jihot Lumban Gaol Joanito Agili Lopo Jonas, Dendy Kainama, Marchel Devid Karema Sarajar, Dewita Kho, Delvian Christoper Krismiyati Kristoko Dwi Hartomo Lea Klarisa Lumban Gaol, Jihot Markus Permadi Mau, Stevanus Dwi Istiavan Maya Sari Mellyuga Errol Wicaksono Merryana Lestari Mira Mira Mira Muhammad Aufal Muhammad Rizky Pribadi Nadya Octavianna Lompoliuw Nahak, Yosef Jeffri Silvanus Nahusona, Ferry Nanle, Zeze Nina Rahayu Nina Setiyawati Ninda Lutfiani Nurrokhman, Nurrokhman Nurtino, Tio Permadi, Markus Picauly, Irma Amy Pratyaksa Ocsa Nugraha Saian Priatna , Wowon Purbaratri, Winny Purnama Harahap, Eka Purwanto - Purwanto Putri, Violita Eka Radius Tanone Ramos Somya Raynaldo Raynaldo Raynaldo Raynaldo, Raynaldo Richard William Kho Riko Yudistira Robert William Ruhulessin Rufina Rahma Ajeng Setyaningsih Safitri, Adila Sakalessy, Afelia Jozalin Elisa Sampoerno Santoso, Fian Julio Santoso, Fian Yulio Santoso, Joseph Teguh Setiyaji, Akhfan Sri Kawuryan, Istiarsi Saptuti Sri Sri Yulianto Joko Prasetyo Sugiman, Marcelino Maxwell Sutarto Wijono Syamsul Arifin Tad Gonsalves Tad Gonsalves Teguh Indra Bayu Teguh Wahyono Theopillus J. H. Wellem Tirsa Ninia Lina Tri Wahyuningsih Trivena Andriani Tukino, Tukino Tumbade, Marcho Oknivan Tungady, Cornelius Arvel Pratama Untung Rahardja Utama, Deffa Ferdian Alif Valentino Kevin Sitanayah Que Walangara Nau, Novriest Umbu Wibowo, Mars Caroline Widyarini, Liza Wilujeng Ayu Nawang Sari Winny purbaratri Wisnu Wibisono, Indra Wiwien Hadikurniawati Yerik Afrianto Singgalen Yessica Nataliani Yos Richard Beeh Yos Richard Beeh Yos Richard Beeh Yudistira, Riko Yuli Agung Suprabowo, Gunawan Yusuf, Natasya Aprila Zakaria, Noor Azura