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Hindriyanto Dwi Purnomo

Universitas Kristen Satya Wacana

Author-ID : 349478

Religion 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 Medicine & Pharmacology Physics Social Sciences Transportation Other

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Analysis Of Spotify Top Songs During Covid-19 Pandemic Atmoko Nugroho; Danny Manongga; Hindriyanto Dwi Purnomo; Hendry Hendry
International Journal of Marketing and Digital Creative Vol. 1 No. 2 (2023): International Journal of Marketing and Digital Creative
Publisher : Research Synergy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/ijmadic.v1i2.1565

During the COVID-19 pandemic, many behaviors or habits have changed, especially in the internet audio-visual field which has increased significantly, one example is Spotify as an audio service provider. Not all songs on Spotify are popular or in the Top Songs. This study aims to examine whether there were differences in popular songs during the pandemic and before the pandemic and to determine the relationship between factors of popular songs on Spotify during the COVID-19 pandemic. The method used is to fetch Spotify songs via the API (Application Programming Interface) with the Spotify Python library. The features obtained are compared with the boxplot. The correlation between the Danceability and Energy features is obtained which ranges from 0.5-0.7, while the other features require further preprocessing because the values are not the same and are empty. This shows that every song that is considered good Danceability and Energy ranges from 0.5 to 0.7, regardless of singer, genre, or other song features.

Design of Batik Motif Detection System Using Deep Learning Method Janinda Puspita Anidya; Hindriyanto Dwi Purnomo
International Journal of Information Technology and Business Vol. 7 No. 2 (2025): April : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.722025.09-14

Batik in Indonesia is growing very rapidly, almost every region or city in Indonesia has a variety of batik motifs. The batik motifs owned by each region are their own wealth and heritage in each region that must be preserved and maintained properly. So the Indonesian people need to collaborate with each other to place batik preservation as a top priority in the field of preserving the nation's culture. Because knowledge of batik motifs in Indonesia is important in order to maintain the culture of the Indonesian nation, including knowledge of batik motifs in each of their respective regions, so there is a need to make it easier for humans to recognize batik motifs in regions in Indonesia quickly and easily. This study aims to build a system model that can assist humans in recognizing batik motifs in Indonesia through this batik motif detection system. This research produces a model that can detect batik motifs from every area in Central Java. The conclusion of this study is a batik detection model that can help and introduce to the public about various batik motifs from each region.Batik in Indonesia is growing very rapidly, almost every region or city in Indonesia has a variety of batik motifs. The batik motifs owned by each region are their own wealth and heritage in each region that must be preserved and maintained properly. So the Indonesian people need to collaborate with each other to place batik preservation as a top priority in the field of preserving the nation's culture. Because knowledge of batik motifs in Indonesia is important in order to maintain the culture of the Indonesian nation, including knowledge of batik motifs in each of their respective regions, so there is a need to make it easier for humans to recognize batik motifs in regions in Indonesia quickly and easily. This study aims to build a system model that can assist humans in recognizing batik motifs in Indonesia through this batik motif detection system. This research produces a model that can detect batik motifs from every area in Central Java. The conclusion of this study is a batik detection model that can help and introduce to the public about various batik motifs from each region.

Sentiment Analysis of Healthcare Services at RSUD Soe Using Machine Learning and Latent Dirichlet Allocation Saekoko, Agatha Marilin; Purnomo, Hindriyanto Dwi; Nataliani, Yessica
Jurnal Ilmiah Sains Volume 26 Issue 1, April 2026
Publisher : Sam Ratulangi University, Manado, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/jis.v26i1.67193

Healthcare services constitute a crucial aspect in improving public well-being. Every individual has the right to receive healthcare services that are of high quality, safe, efficient, and affordable. This study aims to identify and analyze public perceptions and sentiments toward healthcare services at RSUD Soe, as well as to evaluate the performance of several machine learning methods in classifying such sentiments. The data were collected from 278 respondents through a Likert-scale questionnaire that represents perceptions and levels of satisfaction regarding various service aspects. Sentiment analysis was conducted using four machine learning algorithms, namely Naïve Bayes, C4.5, Random Forest, and Support Vector Machine. The results indicate that Naïve Bayes achieved the highest accuracy of 82.14 percent, followed by SVM at 80 percent, Random Forest at 79 percent, and C4.5 at 73.21 percent. This study also applied the Latent Dirichlet Allocation (LDA) method to identify the main themes within public feedback. LDA generated twelve topics reflecting key issues such as waiting time, availability of medical personnel, facility cleanliness, and the attitudes of healthcare staff. The majority of comments exhibited positive sentiment, particularly concerning staff friendliness and service quality. These findings were used to formulate improvement recommendations, including enhancing service quality, increasing the number of medical personnel, and optimizing facilities. This research demonstrates that a data-driven quantitative approach is effective in evaluating healthcare service quality and supporting more targeted decision-making. The results are expected to assist RSUD Soe in continuously and effectively improving service quality.

Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting Tri Wahyuningsih; Ade Iriani; Hindriyanto Dwi Purnomo; Irwan Sembiring
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p29-37

This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing data intricacies and non-linear features, complemented by advanced linear regression offering valuable coefficient interpretations for linear relationships. This research innovatively contributes by harmonizing two distinct methods to create a predictive model for students' exam success. The conclusion emphasizes the merits of an ensemble approach in refining prediction accuracy, recognizing, however, the study's limitations in terms of dataset constraints and external factors. In essence, this study enhances comprehension of predicting student success, offering educators insights to identify and support potentially struggling students.

Trends in sentiment of Twitter users towards Indonesian tourism: analysis with the k-nearest neighbor method Eka Purnama Harahap; Hindriyanto Dwi Purnomo; Ade Iriani; Irwan Sembiring; Tio Nurtino
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p19-28

This research analyzes the sentiment of Twitter users regarding tourism in Indonesia using the keyword "wonderful Indonesia" as the tourism promotion identity. The aim of this study is to gain a deeper understanding of the public sentiment towards "wonderful Indonesia" through social media data analysis. The novelty obtained provides new insights into valuable information about Indonesian tourism for the government and relevant stakeholders in promoting Indonesian tourism and enhancing tourist experiences. The method used is tweet analysis and classification using the K-nearest neighbor (KNN) algorithm to determine the positive, neutral, or negative sentiment of the tweets. The classification results show that the majority of tweets (65.1% out of a total of 14,189 tweets) have a neutral sentiment, indicating that most tweets with the "wonderful Indonesia" tagline are related to advertising or promoting Indonesian tourism. However, the percentage of tweets with positive sentiment (33.8%) is higher than those with negative sentiment (1.1%). This study also achieved training results with an accuracy rate of 98.5%, precision of 97.6%, recall of 98.5%, and F1-score of 98.1%. However, reassessment is needed in the future as Twitter users' sentiment can change along with the development of Indonesian tourism itself.

The Application of Restful Web Service and Json for Poultry Farm Monitoring System Hindriyanto Dwi Purnomo; Dody Agung Saputro; Ramos Somya; Charitas Fibriani
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 1 No. 1 (2016): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v1i1.183

Partnership schema is widely applied in Indonesia poultry farm industry. In this schema, a poultry company cooperates with many breeder partners to raise their chicken. The company sends their field inspection staffs to monitor the growth of the chickens. Large number of breeders with manual process of report, handle, and monitor takes a significant amount of time and efforts. In addition, the data cannot be observed immediately by the company. A poultry farm monitoring system based on the Application Programming Interface (API) is proposed in this research. The system can be used by breeders, breeder partners and field inspection staffs to facilitate the process of reporting, handling and monitoring by the poultry company. The API technology is applied as a data center and a data provider. The combination of RESTful web service and JSON into the API enable the integration can be processed safely as well as simple and easy to use. The proposed system can be applied to complement or replace the existing manual processes on many poultry farms with partnership schema.

Peningkatan Kapasitas Penelitian Guru dan Siswa SMA Melalui Pelatihan Metodologi Penelitian dan Pendampingan Olimpiade Penelitian Siswa Indonesia Andreas A. Sukmana; Sri Kasmiyati; Betty E. Kristiani; Hindriyanto D. Purnomo; Budhi Kristianto; Krismiyati Krismiyati; Teguh I. Bayu; Radius Tanone; Adi Nugroho; Hanita Yulia; Evangs Mailoa; Erwien Christianto
JURNAL PENGABDIAN PAPUA Vol 10 No 1 (2026)
Publisher : LPPM Uncen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31957/jpp.v10i1.5252

Critical and innovative thinking skills are essential requirements for students to meet the global challenges. One strategy to develop these skills is through participation in the Indonesian Student Research Olympiad (OPSI), which requires collaboration between teachers and students in implementing fundamental research concepts. However, the implementation of basic research concepts within the high school curriculum remains limited. Consequently, collaboration with higher education institutions serves as a potential solution, specifically through research training and mentoring. This training initiative aimed to enhance the research competence of teachers and students at SMAN 1 Ambarawa by providing methodology training and mentoring for OPSI research teams, conducted by a faculty team from Satya Wacana Christian University. The program was conducted intensively consisting of research design training and research mentoring for nine student groups. The training and the mentoring were provided by lecturers whose expertise aligned with the specific OPSI proposal topics of each group. This mentoring resulted in an overall improvement in the research capabilities of both teachers and students, with one group successfully won a gold medal at OPSI 2025. In conclusion, this program results in a positive reception, which expressed the hope that similar initiatives can be conducted on a regular basis.

Enhancing Social Value through Orange Technology Adoption in Creative Industry Micro Enterprises Ninda Lutfiani; Hindriyanto Dwi Purnomo; Heru Riza Chakim; Syahrul Mu’Arif Wahid; Oliver Sauntos
ADI Bisnis Digital Interdisiplin Jurnal Vol 6 No 2 (2025): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v6i2.1353

Social media–based digital transformation has become an essential strategy for MSMEs to expand their market reach and strengthen consumer loyalty in the digital economy era. This article presents a conceptual review and empirical synthesis of digital business transformation strategies in MSMEs that utilize social media as the core channel for marketing and customer service. By integrating the Technology Acceptance Model (TAM), Customer Engagement theory, and the Resource-Based View, this paper proposes a strategic framework consisting of (1) digital capabilities, (2) content and engagement, (3) digital after-sales services, and (4) a collaborative ecosystem (platforms and micro-influencers). The literature synthesis indicates that interactive social media activities and responsive services are consistently associated with increased customer engagement and brand loyalty among MSMEs. Practical recommendations and future research directions are provided to support MSMEs in implementing loyalty-oriented digital transformation.

Comparative Study of Classical and Quantum Machine Learning Models: Insights into Quantum Advantage in Materials Informatics Aris Tri Joko Harjanto; Hindriyanto Dwi Purnomo; Hendry
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2733

Quantum Machine Learning (QML) has emerged as a promising paradigm for addressing increasing computational and representational demands in materials informatics. While classical models such as Support Vector Machines (SVM) achieve strong predictive performance, they often struggle to capture complex, highly correlated interactions in high-dimensional materials data. QML addresses this challenge by leveraging quantum-mechanical principles to construct expressive feature embeddings, where prospective quantum advantage lies in generating feature spaces that are difficult to approximate classically. In this study, 1,000 crystalline compounds from the Open Quantum Materials Database (OQMD) are evaluated in a binary classification task based on formation-energy stability. The dataset is normalized, reduced to four dimensions via Principal Component Analysis (PCA), and encoded into quantum circuits. Three QML models—QSVM, VQC, and QNN—are benchmarked against a classical SVM using repeated stratified evaluation. Results show that the classical SVM achieves the highest accuracy (91.8% ± 0.012), followed by QSVM (60.8% ± 0.035), while VQC and QNN perform significantly worse. This gap is driven by limited qubit capacity, encoding inefficiencies, restricted circuit expressivity, and optimization challenges. Nevertheless, QSVM demonstrates stable performance, suggesting that potential quantum advantage may emerge from improved feature encoding and kernel design rather than deeper variational circuits.

Analysis Of Library Visitors' Interest Using Factor Analysis And Discriminant Analysis Hery Santono; Eko Sediyono; Hindriyanto Dwi Purnomo
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/g3vb2a79

The relevance of libraries as learning centers, gathering places for the scientific community, and access points for resources not always available online underscores the importance of understanding the factors that influence library visitor interest. This study aims to analyze the factors impacting visitor interest using Factor Analysis and Discriminant Analysis. The key factors explored include service quality, comfort of facilities, quality of book collections, access to digital technology, and frequency of visits. Data was collected through surveys conducted with 500 library visitors across five different locations over a three-month period. Factor Analysis revealed that comfort factors and access to technology were the most significant variables influencing visitor interest, accounting for 65% of the variance in visitor behavior. Discriminant Analysis further classified visitors into high and low interest groups, showing that library facilities were the primary differentiator between these two groups. The study found that visitors with high interest were more likely to be influenced by the library's physical comfort and technology access, while those with low interest were less engaged with the library's services. This research provides valuable insights for library managers to enhance services, optimize library environments, and incorporate technological advancements to increase visitor engagement. It also contributes to the theoretical understanding of library management by identifying key factors that affect visitor interest, which can inform future strategies in the field. However, this study is limited by its cross-sectional nature, and the results may not be generalizable to other regions or visitor demographics. Future research could explore longitudinal data to assess how visitor preferences evolve over time.

Co-Authors Ade Iriani Adi Nugroho Adimas Tristan Nagara Hartono Adriyanto Juliastomo Gundo Agung Wibowo Agus Priyadi Ahmad Bayu Yadila Andre Kurniawan Andreas A. Sukmana Andrew Aquila Chrisanto Pabendon Andry Ananda Putra Tanggu Mara Andry Tanggu Mara Angela Atik Setiyanti Anton Hermawan Anton Hermawan Anwar, Muchamad Taufiq April Firman Daru April Lia Hananto Aris Puji Widodo Aris Tri Joko Harjanto Arseta, Gama Astawa, I Wayan Aswin Dew Atik Setyanti, Angela Atmoko Nugroho Aziz Jihadian Barid Azzahra Nurwanda Bandung Pernama Baun, Sindy Cristine Bayangkariwati Tacoh, Yuliana Tien Betty E. Kristiani Budhi Kristianto Budi Kristianto Budi Kristianto, Budi C. Leuwol, Sylvie Cahyaningtyas, Christyan Cahyo Dimas K Chandra Halim Chandra, Dian W. Charitas Fibriani Christyan Cahyaningtyas Daniel Kurniawan Daniel Kurniawan Daniel Yeri Kristiyanto Danny Manongga Danu Satria Wiratama Deden Rustiana Dedy Prasetya Kristiadi Didit Budi Nugroho Dody Agung Saputro Dwi Hosanna Bangkalang Edwin Zusrony Eka Purnama Harahap 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 Galih Putra Cesna 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 Hendry Hendry Heni Pujiastuti Hermanto Abraham, Rendy Heru Riza Chakim Hery Santono 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 Juliastomo Gundo, Adriyanto 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 Ninda Lutfiani Nurrokhman, Nurrokhman Nyree Ani Oliver Sauntos Permadi, Markus Picauly, Irma Amy Pratyaksa Ocsa Nugraha Saian Priatna , Wowon 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 Saekoko, Agatha Marilin Safitri, Adila Sakalessy, Afelia Jozalin Elisa Sampoerno Santoso, Fian Julio Santoso, Fian Yulio Santoso, Joseph Teguh Setiyaji, Akhfan Setyanti, Angela Atik Sri Kasmiyati Sri Kawuryan, Istiarsi Saptuti Sri Sri Yulianto Joko Prasetyo Sugiman, Marcelino Maxwell Sutarto Wijono Syahrul Mu’Arif Wahid Syamsul Arifin Tad Gonsalves Tad Gonsalves Teguh I. Bayu Teguh Indra Bayu Teguh Wahyono Theopillus J. H. Wellem Tio Nurtino 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 Vinsensius Wijaya 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 Yudistira, Riko Yuli Agung Suprabowo, Gunawan Yusuf, Natasya Aprila Zakaria, Noor Azura

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