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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

Author-ID : 349478

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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Enhancing Multi-Output Time Series Forecasting with Encoder-Decoder Networks Kristoko Dwi Hartomo; Joanito Agili Lopo; Hindriyanto Dwi Purnomo
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 2 (2023): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.2.195-213

Background: Multi-output Time series forecasting is a complex problem that requires handling interdependencies and interactions between variables. Traditional statistical approaches and machine learning techniques often struggle to predict such scenarios accurately. Advanced techniques and model reconstruction are necessary to improve forecasting accuracy in complex scenarios. Objective: This study proposed an Encoder-Decoder network to address multi-output time series forecasting challenges by simultaneously predicting each output. This objective is to investigate the capabilities of the Encoder-Decoder architecture in handling multi-output time series forecasting tasks. Methods: This proposed model utilizes a 1-Dimensional Convolution Neural Network with Bidirectional Long Short-Term Memory, specifically in the encoder part. The encoder extracts time series features, incorporating a residual connection to produce a context representation used by the decoder. The decoder employs multiple unidirectional LSTM modules and Linear transformation layers to generate the outputs each time step. Each module is responsible for specific output and shares information and context along the outputs and steps. Results: The result demonstrates that the proposed model achieves lower error rates, as measured by MSE, RMSE, and MAE loss metrics, for all outputs and forecasting horizons. Notably, the 6-hour horizon achieves the highest accuracy across all outputs. Furthermore, the proposed model exhibits robustness in single-output forecast and transfer learning, showing adaptability to different tasks and datasets. Conclusion: The experiment findings highlight the successful multi-output forecasting capabilities of the proposed model in time series data, with consistently low error rates (MSE, RMSE, MAE). Surprisingly, the model also performs well in single-output forecasts, demonstrating its versatility. Therefore, the proposed model effectively various time series forecasting tasks, showing promise for practical applications. Keywords: Bidirectional Long Short-Term Memory, Convolutional Neural Network, Encoder-Decoder Networks, Multi-output forecasting, Multi-step forecasting, Time-series forecasting

Sentiment Analysis of e-Government Service Using the Naive Bayes Algorithm Winny purbaratri; Hindriyanto Dwi Purnomo; Danny Manongga; Iwan Setyawan; Hendry Hendry
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3272

E-Government which involves the use of communication and information technology to provide Public services have three obstacles. One of these obstacles is the implementation of e-Government by autonomous regional governments is still carried out individually. Apart from that, implementing the website regions are also not supported by efficient management systems and work processes, this is partly the case This is largely due to the lack of preparation of regulations, procedures and limited resources man. Apart from that, many local governments consider implementing e-Government only involves developing local government websites. More precisely, the implementation of e-Government It is only limited to the maturity stage and ignores the three other important stages that need to be completed. The aim of this research is to determine the level of public approval for government application services. This research uses the Naive Bayes Classifier approach as the methodology. The data sources used in this research consist of user reviews and comments obtained from Google Play Store. The results of this investigation produce a level of precision The highest is achieving a score of 83%. Additionally it shows an accuracy rate of 83%,levelcompleteness is 100%, and F-measure is 90.7%.

Optimasi dan Perancangan Antena Menggunakan Metode Modified Efficient K-Nearest Neighbors Deden Rustiana; Nina Rahayu; Hindriyanto Dwi Purnomo; Ahmad Bayu Yadila; Hendra Kusumah
ICIT Journal Vol 10 No 2 (2024): Agustus 2024
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/icit.v10i2.3204

Untuk memastikan akurasi dan mencegah perilaku machine learning yang tidak diinginkan terjadi ketika model machine learning memberikan prediksi akurat untuk data pelatihan tetapi tidak untuk data baru yang biasa disebut Overfitting, teknik machine learning yang efektif biasanya dilatih pada kumpulan data besar. Namun, ketika pengumpulan data rumit, kumpulan data yang besar menghambat penyebaran teknik machine learning. Algoritma K-Nearest Neighbors (KNN) ditingkatkan dalam penelitian ini untuk mengatasi masalah dan menyajikan pendekatan machine learning yang unik sehingga dapat mengekstraksi lebih banyak fitur dari kumpulan data yang besar. Metode ini bekerja 5 hingga 30 kali lebih cepat daripada teknik machine learning konvensional seperti jaringan syaraf tiruan (ANN) dan pengoptimalan Bayesian. Parameter antena digunakan untuk mengoptimalkan kemudian dioptimalkan menggunakan metode yang disarankan, dan cabang terpisah dibuat untuk menjalankan alat simulasi (seperti HFSS) dan memperbarui dataset saat pelatihan daripada membuatnya sebelumnya. Empat contoh antena lainnya, serta machine learning tambahan dan teknik berbasis gradien, digunakan untuk mendukung validitas dan efektivitas pendekatan yang disarankan. Kesimpulannya, metode ini disarankan dapat menghasilkan desain antena ideal dan harga terjangkau.

Deteksi Cacat pada Isolasi Trafo Secara Visual menggunakan Algoritma Convolutional Neural Network (CNN) Faudisyah, Alfendio Alif; Hartomo, Kristoko Dwi; Purnomo, Hindriyanto Dwi
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 4 (2023): OCTOBER-DECEMBER 2023
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i4.1067

Transformer insulation is a dielectric material that has the function of selling two or more voltage electrical conductors. Damage to the transformer insulation will cause interference with the performance of the transformer so that it can cause the transformer to experience operational failure or even damage. This research builds a system that can classify defective and normal transformer insulation images. The Convolutional Neural Network method is implemented in model building. The research method begins with conducting research planning, dataset collection, data preprocessing, classification of development models, training models, as well as testing and evaluation. Based on the test results with standardized data size 180 x 180 x 3 pixels, it produces an accuracy of 0.9913 for training, 0.9884 for testing, and 1.00 for evaluation. Test results with standardized data size 240 x 240 x 3 pixels produce an accuracy of 0.9798 for training, 0.9651 for testing, and 0.94 for evaluation. Based on the research that has been done, shows that differences in data standardization can affect the results of the model performance

Analisis Pengguna Media Sosial Terhadap Isu UU Cipta Kerja Menggunakan SNA dan Naive Bayes Mau, Stevanus Dwi Istiavan; Sembiring, Irwan; Purnomo, Hindriyanto
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

In research about analysis issue analysis, UU Cipta Kerja aims to investigate aktor which has the most influence in the discussion on the network is based on the analysis of the most popular centrality values on the issues law copyright this working. Research is in use of the method of Social Network Analysis ( SNA ). The data in minutely as many as 1686 nodes and 1403 edges in extract through the API Twitter with the help of application WinPython and Netlytic with a period 08 October 2020 - 05 July 2021. The result of this research showed that account @BEMUI_ Official is account the most popular with the Degree Centrality 944, value Betweenness Centrality 640042.0. But on a calculation Closeness Centrality aktor @BEMUI_Official having value 0.701235, therefore nodes who have a centrality highest do not necessarily have the value that both in terms of the dissemination of information.

Prediksi Tingkat Kesembuhan Pasien Covid-19 Berdasarkan Riwayat Vaksin Menggunakan Metode Naïve Bayes Gudiato, Candra; Prasetyo, Sri Yulianto Joko; Purnomo, Hindriyanto Dwi
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Covid-19 has shocked the world since it first appeared at the end of December 2019. At the beginning of 2022, the global community is more prepared to face the COVID-19 pandemic, especially with the mass vaccination program in countries around the world, including Indonesia. The next issue is how effective the vaccine is in dealing with the COVID-19 virus. The main parameter used is to see the recovery rate of patients affected by COVID-19 based on the history of vaccine doses that have been received by the patient. In this study using data mining techniques, namely using the Naïve Bayes algorithm. The test results show the accuracy of the Naïve Bayes algorithm is 98.14%. The prediction results show that the recovery rate of patients who have received the vaccine, either dose 1, dose 2, or dose 3 (booster) is higher than those who have not been vaccinated at all (dose 0). The results of this study are expected to provide an overview to the public and the government about the benefits of vaccination in dealing with the Covid-19 virus.

Implementasi Transfer Learning Pada Algoritma Convolutional Neural Network untuk Mengklasifikasikan Image Objek Wisata Mira, Mira; Sembiring, Irwan; Purnomo, Hindriyanto Dwi
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

This study classifies the image of a tourist attraction with 9 labels sky, tree, mountain, water, street, temple, garden, stone and ricefield. The results of multi-label labeling can be used to see the frequency and recommendations of tourist attractions in Central Java, and build a transfer learning model to determine the accuracy value. Classification with multi-label images has its own complexity in the labeling process and few people use it. Testing and evaluating the model uses the equation of accuracy and f-1 score. Several previous researchers also stated that the higher the amount of training data and the number of epochs per step, the higher the accuracy produced. Based on the results of training and evaluation of the four training processes, that 210 data using bs 8, lr 1e-3 and epoch 50 showed an accuracy of 0.8598 with a loss of 0.3245, while 290 data with bs 16, lr 1e-3 and epoch 50 showed an accuracy of 0.8685. with a loss of 0.2903. Then 594 data with bs 32, lr 1e-3 and epoch 50 showed an accuracy of 0.8852 with a loss of 0.2756, and 1000 data with bs 46, lr 1e-3 and epoch 50 showed an accuracy of 0.8833 with a loss of 0.2863. This can answer the statement that the greater the number of datas, the higher the accuracy produced, so that the transfer learning model on the ResNet-50 architecture with multi-label image datas can be applied by showing accuracy results close to the accuracy value on ResNet-50 in the imagenet project. In addition, the contribution of this research is to provide recommendations for potential tourist objects in Central Java, namely tourism objects with the theme of nature, then tourism processed by human hands such as historical places, cultural heritage and family recreation areas.

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

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.

A three-step combination strategy for addressing outliers and class imbalance in software defect prediction Rizky Pribadi, Muhammad; Dwi Purnomo, Hindriyanto; Hendry, Hendry
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2987-2998

Software defect prediction often involves datasets with imbalanced distributions where one or more classes are underrepresented, referred to as the minority class, while other classes are overrepresented, known as the majority class. This imbalance can hinder accurate predictions of the minority class, leading to misclassification. While the synthetic minority oversampling technique (SMOTE) is a widely used approach to address imbalanced learning data, it can inadvertently generate synthetic minority samples that resemble the majority class and are considered outliers. This study aims to enhance SMOTE by integrating it with an efficient algorithm designed to identify outliers among synthetic minority samples. The resulting method, called reduced outliers (RO)-SMOTE, is evaluated using an imbalanced dataset, and its performance is compared to that of SMOTE. RO-SMOTE first performs oversampling on the training data using SMOTE to balance the dataset. Next, it applies the mining outlier algorithm to detect and eliminate outliers. Finally, RO-SMOTE applies SMOTE again to rebalance the dataset before introducing it to the underlying classifier. The experimental results demonstrate that RO-SMOTE achieves higher accuracy, precision, recall, F1-score, and area under curve (AUC) values compared to SMOTE.

Sistem Deteksi Anomali Pada Transformator Menggunakan Dissolved Gas Analysis Dengan Metode K-Nearest Neighbour Kurniawan, Andre; 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.v8i1.7034

The transformer is the most important part of the electric power system, therefore maintenance needs to be carried out to prevent the emergence of anomalies in the transformer. Dissolved Gas Analysis (DGA) is a method for detecting anomalies in transformers. DGA is used to test the condition of the insulating oil in transformers by taking samples of the insulating oil. If an anomalous event occurs in a transformer, the resulting gas concentration will vary depending on the type of anomalous event in the transformer. The main problem underlying this research is the inability of previously existing anomaly detection systems to provide an optimal level of accuracy, traditional methods or approaches used also face obstacles in interpreting complex data from dissolved gas analysis. The aim of the research carried out is to be able to design an anomaly detection system on Transformers using DGA and to see the level of accuracy of the existing DGA method using KNN. In this research, the anomaly detection system on the transformer resulted in the highest level of accuracy being 94% using the key gas method and the lowest level of accuracy being 79% using the Doernenburg Ratio method. The conclusion of this research is that it is able to create a system that can make it easier to analyze anomalies in transformers, and can be used as an alternative method for determining the condition of transformers.

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

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