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All Journal dCartesian: Jurnal Matematika dan Aplikasi Media Statistika Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Kubik Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Ekonomi dan Studi Pembangunan (Journal of Economics and Development Studies) Jurnal Mercumatika : Jurnal Penelitian Matematika dan Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Abdi Insani Indonesian Journal of Data and Science Jurnal Sains dan Edukasi Sains SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) Jurnal Pendidikan JAMBURA JOURNAL OF PROBABILITY AND STATISTICS ADPEBI International Journal of Business and Social Science Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Akuntansi dan Keuangan Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Jurnal Pendidikan Indonesia (Japendi) Jurnal Kedokteran STM (Sains dan Teknologi Medik) Eduvest - Journal of Universal Studies Multifinance KISA INSTITUE : Journal of Economics, Accounting, Business, Management, Engineering and Society Adpebi International Journal of Multidisciplinary Sciences d'Cartesian: Jurnal Matematika dan Aplikasi SJME (Supremum Journal of Mathematics Education)
Bambang Susanto
Universitas Kristen Satya Wacana
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Simple Forward Finite Difference for Computing Reproduction Number of COVID-19 in Indonesia During the New Normal Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Susanto, Bambang; Sardjono, Yohanes
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 5, No 1 (2021): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v5i1.3468

Abstract

The research purpose shown in this article is describing the time dependent reproduction number of coronavirus called by COVID-19 in the new normal period  for 3 types areas, i.e. small, medium and global areas by considering the number of people in these areas.  It is known that in early June 2020, Indonesia has claimed to open activities during the pandemic with the new normal system. Though the number of COVID-19 cases is still increasing in almost infected areas, normal activities are coming back with healty care protocols where public areas are opened as usual with certain restrictions. In order to have observations of spreading impact of COVID-19, the basic reproduction number (Ro)  i.e. the reproduction number (Ro) is the ratio between 2 parameters of SIR model where SIR stands for Susceptible individuals, Infected individuals, and Recovered individuals respectively. The reproduction numbers  are computed as discrete values depending on time. The used research method is  finite difference scheme for computing rate of change parameters in SIR models based on the COVID-19 cases in Indonesia (global area), Jakarta (medium area) and Salatiga (small area) by considering the number of people in these areas respectively. The simple forward finite difference is employed to the SIR model to have time dependent of parameters. The second approach is using the governing linear system to obtain the values of parameter daily. These parameters are computed for each day such that the values of Ro are obtained as function of time. The research result shows that 3 types areas give the same profiles of parameters that the rate of changes of reproduction numbers are decreasing with respect to time. This concludes that the reproduction numbers are most likely decreasing.
GRG Non-Linear and ARWM Methods for Estimating the GARCH-M, GJR, and log-GARCH Models Nugroho, Didit Budi; Panjaitan, Lam Peter; Kurniawati, Dini; Kholil, Zaini; Susanto, Bambang; Sasongko, Leopoldus Ricky
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 2 (2022): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i2.7694

Abstract

Numerous variants of the basic Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have been proposed to provide good volatility estimating and forecasting. Most of the study does not work Excel’s Solver to estimate GARCH-type models. The first purpose of this study is to provide the capability analyze of the GRG non-linear method built in Excel’s Solver to estimate the GARCH models in comparison to the adaptive random walk Metropolis method in Matlab by own codes. The second contribution of this study is to evaluate some characteristics and performance of the GARCH-M(1,1), GJR(1,1), and log-GARCH(1,1) models with Normal and Student-t error distributions that fitted to financial data. Empirical analyze is based on the application of models and methods to the DJIA, S&P500, and S&P CNX Nifty stock indices. The first empirical result showed that Excel’s Solver’s Generalized Reduced Gradient (GRG) non-linear method has capability to estimate the econometric models. Second, the GJR(1,1) models provide the best fitting, followed by the GARCH-M(1,1), GARCH(1,1), and log-GARCH(1,1) models. This study concludes that Excel’s Solver’s GRG non-linear can be recommended to the practitioners that do not have enough knowledge in the programming language in order to estimate the econometrics models. It also suggests to incorporate a risk premium in the return equation and an asymmetric effect in the variance equation. 
Pembelajaran Vektor Untuk Klasifikasi Data Pada Bidang Parhusip, Hanna Arini; Susanto, Bambang; Linawati, Lilik; Trihandaru, Suryasatriya; Sardjono, Yohanes
SJME (Supremum Journal of Mathematics Education) Vol 4 No 2 (2020): Supremum Journal of Mahematics Education
Publisher : Fakultas Keguruan dan Ilmu Pendidikan Universitas Singaperbangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/sjme.v4i2.3515

Abstract

Tujuan penelitian ini adalah penyusunan hyperplane untukmemisahkan data yang mempunyai 2 kelas dan bersifat linear padabidang datar sebagai pembelajaran vektor untuk klasifikasi data.Adapun metode yang digunakan adalah pre-Support Vector Machine(SVM). Metode ini mencari garis (hyperplane) terbaik yangmemisahkan data dan memberi ruang antar 2 kelas data dimana ruangpemisah tersebut tidak boleh memuat data serta ruang tersebutmerupakan margin maksimal. Langkah awal adalah menduga garispemisah (hyperplane) awal melalui titik O. Dengan mengambil salahsatu titik data yang menjadi titik referensi, disusun vektor dari Oterhadap titik referensi dan garis melalui titik referensi sebagai bataspertama margin. Kemudian dibentuk vektor arah dari titik O yangtegak lulus terhadap garis awal (hyperplane). Selanjutnya vektorproyeksi dibentuk dari titik referensi terhadap vektor arah sehinggavektor arah dan vektor proyeksi berhimpit (searah). Penyusunanmargin diperoleh dengan menyusun garis yang pararel terhadap garisawal sebagai hyperplane serta berjarak 2 kali dengan panjang vektorproyeksi tersebut. Hyperplane terbaik diperoleh secara manual denganmengatur batas kedua dari margin yang diperoleh dengan menggambargaris melalui suatu titik data pada kelas ke-2 dengan jarak terdekat danpararel terhadap garis yang melalui titik referensi dari data kelas ke-1.
Human Capital Decision Intelligence (HCDI) architecture in microbiology laboratory based on machine learning and operations research models Trihandaru, Suryasatriya; Susetyo, Yosia Adi; Parhusip, Hanna Arini; Susanto, Bambang
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.1676

Abstract

The Human Capital Decision Intelligence (HDCI) system integrates human-computer interaction in a microbiology laboratory that uses machine learning and operational research to classify new tasks and then recommend assignments to each person. The models evaluated in building this system are Support Vector Machine, Gaussian Naive Bayes, Multinomial Logistic Regression, and Artificial Neural Network. The results of the research show that the ANN model is the most consistent and reliable across various training ratios, as indicated by the model's goodness parameters. The selected ANN model is combined with a linear programming approach to optimize workload distribution. The integrated system successfully manages new job scenarios and recommends staff based on competencies and availability. It also ensures assignments do not exceed maximum workload limits and finds alternatives when key staff are unavailable. The implementation of the HDCI system has a positive impact on various factors, including the fair distribution of tasks, enhanced staff performance monitoring, and significantly improved operational efficiency and human resource management in the microbiology laboratory. The system is designed to be easy to use and support collaboration between laboratory staff and computational models. The system is not only advanced in supporting personnel management decision-making, but it can also demonstrate how artificial intelligence and operations research systems can be combined to address the needs of the microbiology laboratory environment.
ENHANCING VOLATILITY MODELING WITH LOG-LINEAR REALIZED GARCH-CJ: EVIDENCE FROM THE TOKYO STOCK PRICE INDEX Nugroho, Didit Budi; Putri, Zefania Sasongko; Susanto, Bambang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0881-0894

Abstract

This study compares the Log-linear Realized GARCH (LRG) and its extension with Continuous and Jump components (LRG-CJ) in modeling the volatility of financial assets, using daily data from the Tokyo Stock Price Index (TOPIX) over 2004–2011. The urgency arises from the need for more accurate volatility models during turbulent periods such as the 2008 Global Financial Crisis and the 2011 Great East Japan Earthquake, where markets exhibit both smooth fluctuations and abrupt jumps. Methodologically, the LRG-CJ framework introduces a novel integration of continuous and jump decomposition into the LRG structure, offering an applied innovation to high-frequency volatility modeling. Realized Volatility (RV) was calculated from 1-, 5-, and 10-minute intraday data and decomposed into continuous and jump components. Parameter estimation employed the Adaptive Random Walk Metropolis (ARWM) within a Markov Chain Monte Carlo algorithm, while model performance was assessed using multiple information criteria and out-of-sample forecast evaluations. The empirical results reveal that incorporating continuous and jump components improves volatility modeling accuracy, forecasting, and Value-at-Risk estimation. However, these benefits are frequency-dependent: the LRG-CJ model shows superior in-sample fit for 1-minute RV but provides the strongest out-of-sample forecasting and risk prediction at lower frequencies (5- and 10-minute intervals). This highlights that while jumps are best identified at ultra-high frequencies, their predictive value is most effectively captured in slightly aggregated data. The originality of this study lies in being the first empirical application of LRG-CJ, demonstrating how continuous–jump decomposition interacts with the dual-equation structure of LRG, which has not been examined in TGARCH or APARCH contexts. Limitations include sensitivity to microstructure noise in very high-frequency data and computational challenges in parameter convergence. Overall, the findings underscore the novelty and practical importance of the LRG-CJ framework for risk management, offering actionable guidance for aligning volatility models with data frequency
DIGITAL TRANSFORMATION IN INDONESIAN SMES: DRIVERS, BARRIERS, AND PERFORMANCE OUTCOMES Aripin, Zaenal; Susanto, Bambang; Agusiady, Ricky
Journal of Economics, Accounting, Business, Management, Engineering and Society Vol. 1 No. 11 (2024): KISA INSTITUE : October 2024
Publisher : PT. Kreatif Indonesia Satu

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Abstract

Background: Indonesian small and medium enterprises (SMEs) constitute 99.9% of all businesses and contribute significantly to national GDP. However, digital adoption remains fragmented, with only 16% achieving advanced integration levels. The acceleration of digitalization post-pandemic has created unprecedented pressure on SMEs to transform digitally while simultaneously facing substantial resource, capability, and infrastructure constraints unique to emerging market contexts. Aims: This research investigates the multifaceted dynamics of digital transformation among Indonesian SMEs by examining: (1) primary drivers compelling digital adoption, (2) critical barriers impeding transformation efforts, and (3) performance outcomes associated with varying digital maturity levels. The study aims to develop a contextualized framework explaining digital transformation patterns in resource-constrained emerging market environments. Research Method: A mixed-methods design combined quantitative survey analysis of 287 SMEs across manufacturing, retail, services, and technology sectors with qualitative interviews of 15 business owners. Data collection spanned Jakarta, Surabaya, and Bandung during March-August 2024. Partial Least Squares Structural Equation Modeling (PLS-SEM) analyzed quantitative relationships while thematic analysis examined qualitative insights, enabling methodological triangulation. Results and Conclusion: Findings reveal customer expectations (β=0.42, p<0.001) as the strongest adoption driver, followed by competitive pressures (β=0.38) and supply chain requirements (β=0.31). Financial constraints emerged as the most cited barrier (73% of respondents), alongside skills gaps (67%) and technical complexity (58%). Digital maturity demonstrates significant positive correlations with operational efficiency improvements (r=0.48), market expansion (r=0.52), and revenue growth (r=0.56). Three distinct transformation archetypes emerged: Compliance-Driven adopters (38%), Strategic Adopters (29%), and Pioneering Transformers (33%). Contribution: This study extends Technology-Organization-Environment (TOE) framework application in emerging markets by demonstrating organizational learning capabilities as critical mediators between external pressures and adoption outcomes. The identification of distinct transformation archetypes reveals heterogeneity in organizational responses, contradicting institutional isomorphism predictions. Findings inform both SME strategic planning and policy interventions supporting inclusive digital economy development.
EXPLORING THE ROLE OF CORPORATE GOVERNANCE IN ENHANCING THE TRANSPARENCY OF BANKING INSTITUTIONS Aripin, Zaenal; Susanto, Bambang; Agusiady, Ricky
Journal of Economics, Accounting, Business, Management, Engineering and Society Vol. 1 No. 12 (2024): KISA INSTITUE : November 2024
Publisher : PT. Kreatif Indonesia Satu

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Abstract

Background:Corporate governance is critical for banking stability, with governance failures exposed as root causes of institutional collapse in financial crises. Aims:This study explores corporate governance's role in enhancing transparency and performance of banking institutions. Research Method:Using mixed-methods, we examined governance structures across 30 banks in diverse regulatory environments, analyzing governance metrics, performance indicators, and stakeholder interviews. Results and Conclusion:Banks with independent boards showed 15% higher ROE and 22% lower NPL ratios. Transparency correlated strongly with market valuations. Effectiveness varies across contexts with cultural and regulatory moderators. Contribution:The research elucidates mechanisms through which governance influences banking performance and provides practical frameworks for strengthening governance systems.  
WORK-LIFE BALANCE AND EMPLOYEE PRODUCTIVITY IN INDONESIAN SERVICE SECTOR Susanto, Bambang; Agusiady, Ricky; Aripin, Zaenal
Journal of Economics, Accounting, Business, Management, Engineering and Society Vol. 1 No. 7 (2024): KISA INSTITUE : June 2024
Publisher : PT. Kreatif Indonesia Satu

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Abstract

Background: Work-life balance has become increasingly critical as Indonesian service sector employees face mounting pressures from extended working hours and digital connectivity expectations. Aims: This study investigates the relationship between work-life balance and employee productivity in Indonesian service organizations. Research Method: Survey-based quantitative approach involving 245 employees from banking, telecommunications, and hospitality sectors across Jakarta, Surabaya, and Bandung. Results and Conclusion: Employees reporting good work-life balance demonstrated 28 percent higher productivity scores and 35 percent lower turnover intentions. Flexible working arrangements and supportive organizational culture emerged as key enablers. Contribution: Research provides evidence-based recommendations for Indonesian service organizations to enhance productivity through improved work-life balance policies.  
ANALYSIS OF THE IMPACT OF ERP IMPLEMENTATION ON PROCESS OPTIMIZATION GLOBAL MARKETING AND SALES AT PT SINKONA INDONESIA LESTARI Hersusetiyati, Hersusetiyati; Nugraha, Rian Nanda; Algunadi, Muhammad; Susanto, Bambang
Multifinance Vol. 3 No. 2 (2025): Multifinance
Publisher : PT. Altin Riset Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61397/mfc.v3i2.449

Abstract

This research aims to analyze the implementation of the Enterprise Resource Planning (ERP) system at PT Sinkona Indonesia Lestari in the Marketing and International Sales unit. The implementation of ERP is expected to improve operational efficiency and provide better services to consumers, ultimately generating added value and maximizing benefits for all company stakeholders. This study employed a qualitative research method with an interactive qualitative approach. Data were collected through interviews, observations, and documentation studies. The results indicate that the ERP system implementation at PT Sinkona Indonesia Lestari has been running well. The ERP system helps in completing tasks, improving information quality, unifying the company's units, and optimizing operational performance. These findings are supported by interview data from key informants regarding ERP implementation, success factors, and employee adaptation to the ERP system. In conclusion, the implementation of ERP positively impacts the effectiveness and efficiency of the marketing and international sales unit's operations.
Comparison of k-Nearest Neighbor and Naive Bayes Methods for SNP Data Classification Denny Indrajaya; Adi Setiawan; Bambang Susanto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1758

Abstract

In an accident, sometimes the identity of a person who has an accident is hard to know, so it is necessary to use biological data such as Single Nucleotide Polymorphism (SNP) data to identify the person's origin. This research aims to compare the accuracy and the F1 score of the k-Nearest Neighbor method and the Naive Bayes method in classifying SNP data from 120 people who divide into groups, namely European (CEU) and Yoruba (YRI). Determination of the best method based on the average value of accuracy and the average value of F1 score from 1000 iterations with various percentage distributions of training datasets and testing datasets. In this research, the selection of SNP locations for the classification process was carried out by correlation analysis. The average accuracy obtained for the k-Nearest Neighbor method with the value of k=31 is 98.38% where the average F1 score is 98.39% while the Naive Bayes method obtained the average accuracy of 96.74% and the average F1 score of 96.63%. In this case, the k-Nearest Neighbor method is better than the Naive Bayes method in classifying SNP data to determine the origin of a person's ancestor tends to be from CEU or YRI.
Co-Authors Adi Setiawan Adrianus Herry Heriadi Agus Priyono Alfagustina, Yumita Cristin Alfida Tegar Nurani Algunadi, Muhammad Alicia Anggelia Lumbantoruan Alz Danny Wowor Asido Saragih Awik Hidayati Carolina Febe Ronicha Putri Denny Indrajaya Didit B Nugroho Didit Budi Nugroho Dini Kurniawati Dudi Rudianto Dwi Tristianto Egidius Saut Poltak Situmorang Elsa Septyana Endang Ruswanti Eva Rachmawati Faldy Tita Faundra, Alvi Haay, Happy Alyzhya Hanna Arini Parhusip Hanna Arini Parhusip Herdiyanti, Gita Hersusetiyati, Hersusetiyati Johanes Dian Kurniawan Kezia Natalia Putri Prasetia Khalingga, M Ariq Khalingga, Muhammad Ariq Kholil, Zaini Kurniawan, Johanes Dian Laurentia Nindya Sari Prameswara Lenox Larwuy Leopoldus Ricky Sasongko Lestari, Dina Aulia Lilik Linawati Maria Anita masipupu, Frangky Aristiadi Mince M. M. Rosely Modjo, Marchella Ellena Nugraha, Rian Nanda Nuryani, Dwi Panjaitan, Lam Peter Petrus Priyo Santosa Prasetia, Kezia Natalia Putri PURWANTI PURWANTI Putri, Zefania Sasongko Ratnawati, Aryanti Rebecca Rorimpandey Ricky Agusiady Rima Dwijayanty Rinadi, Geraldus Anggoro Rorimpandey, Rebecca Saepudin Saepudin Saragah R Pratama SARI, EMMA NOVITA Setiawan, Audita Sukadwilinda Suroto, Agung Suryasatriya Trihandaru Susetyo, Yosia Adi Syafi’i, Syafi’i Syarifah harahap Tanujaya, Lukhia Britanthia Christina Tri Pujadi Susilo Tundjung Mahatma Vania Beatrice Liwandouw Visher Laja Jaja wahyu ngestisari Wulandari, Nadya Putri Yohanes Sardjono Yohanes Sardjono, Yohanes Yusria, Anna Zaenal Aripin