Garuda - Garba Rujukan Digital

Mengukur Tingkat Keselarasan Information Technology dan Bisnis (Studi Kasus Perusahaan Start-up Digital Wilayah Jawa) Airlangga, Gregorius
Jurnal Buana Informatika Vol 9, No 2 (2018): Jurnal Buana Informatika Volume 9 Nomor 2 Oktober 2018
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v9i2.1485

Abstract. The alignment between information technology (IT) and business becomes a main issue for a digital start-up which adopts technology as a support for the business activities. As one of the growing business in Indonesia, this organization gets special attention from the government that it will be able to become the backbone of the nation's economy. This has led to a necessity of measuring the alignment level between IT and business at digital start-ups in order to describe the conditions for developing strategies to increase competitive benefits. According to that necessity, the research on the assessment of the alignment between IT and business is conducted by using the Luftman SAM (Strategic Alignment Maturity) model to 30 samples of IT start-up companies from various business fields in Java where the center of digital start-up exists in Indonesia. Based on the results of this study, it was found that the average digital start-up has a pretty good alignment level of 3.45. This shows that the strategic alignment between IT and business is starting to integrate in all functional units. Keywords: alignment maturity, Luftman, SAM, start-up.Abstrak. Keselarasan teknologi informasi (IT) dan bisnis telah menjadi masalah utama start-up digital yang memanfaatkan teknologi sebagai penunjang dalam kegiatan bisnis. Sebagai salah satu organisasi yang marak berkembang di Indonesia, organisasi ini mendapatkan perhatian khusus dari pemerintah untuk mampu menjadi tulang punggung perekonomian bangsa. Hal ini menyebabkan perlunya pengukuran tingkat keselarasan IT dan bisnis pada start-up digital agar dapat menggambarkan kondisi keselarasan yang terjadi sehingga mampu menyusun strategi untuk semakin meningkatkan keuntungan kompetitif. Berdasarkan kebutuhan ini peneliti melakukan penilaian keselarasan antara IT dan bisnis dengan menggunakan model Luftman SAM (Strategic Alignment Maturity) terhadap 30 sampel perusahaan start-up IT dari berbagai bidang bisnis yang berada di wilayah Jawa dimana pusat pertumbuhan start-up digital di Indonesia berada. Berdasarkan hasil penelitian ini didapatkan bahwa rata-rata start-up digital memiliki tingkat keselarasan yang cukup baik yakni sebesar 3,45. Hal ini menunjukkan bahwa keselarasan strategis antara IT dan bisnis mulai terbentuk dan terintegrasi di seluruh unit fungsional. Kata Kunci: keselarasan IT dan bisnis, Luftman, SAM, start-up.

Mengukur Tingkat Keselarasan Information Technology dan Bisnis (Studi Kasus Perusahaan Start-up Digital Wilayah Jawa) Gregorius Airlangga
Jurnal Buana Informatika Vol. 9 No. 2 (2018): Jurnal Buana Informatika Volume 9 Nomor 2 Oktober 2018
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v9i2.1485

Abstract. The alignment between information technology (IT) and business becomes a main issue for a digital start-up which adopts technology as a support for the business activities. As one of the growing business in Indonesia, this organization gets special attention from the government that it will be able to become the backbone of the nation's economy. This has led to a necessity of measuring the alignment level between IT and business at digital start-ups in order to describe the conditions for developing strategies to increase competitive benefits. According to that necessity, the research on the assessment of the alignment between IT and business is conducted by using the Luftman SAM (Strategic Alignment Maturity) model to 30 samples of IT start-up companies from various business fields in Java where the center of digital start-up exists in Indonesia. Based on the results of this study, it was found that the average digital start-up has a pretty good alignment level of 3.45. This shows that the strategic alignment between IT and business is starting to integrate in all functional units. Keywords: alignment maturity, Luftman, SAM, start-up.Abstrak. Keselarasan teknologi informasi (IT) dan bisnis telah menjadi masalah utama start-up digital yang memanfaatkan teknologi sebagai penunjang dalam kegiatan bisnis. Sebagai salah satu organisasi yang marak berkembang di Indonesia, organisasi ini mendapatkan perhatian khusus dari pemerintah untuk mampu menjadi tulang punggung perekonomian bangsa. Hal ini menyebabkan perlunya pengukuran tingkat keselarasan IT dan bisnis pada start-up digital agar dapat menggambarkan kondisi keselarasan yang terjadi sehingga mampu menyusun strategi untuk semakin meningkatkan keuntungan kompetitif. Berdasarkan kebutuhan ini peneliti melakukan penilaian keselarasan antara IT dan bisnis dengan menggunakan model Luftman SAM (Strategic Alignment Maturity) terhadap 30 sampel perusahaan start-up IT dari berbagai bidang bisnis yang berada di wilayah Jawa dimana pusat pertumbuhan start-up digital di Indonesia berada. Berdasarkan hasil penelitian ini didapatkan bahwa rata-rata start-up digital memiliki tingkat keselarasan yang cukup baik yakni sebesar 3,45. Hal ini menunjukkan bahwa keselarasan strategis antara IT dan bisnis mulai terbentuk dan terintegrasi di seluruh unit fungsional. Kata Kunci: keselarasan IT dan bisnis, Luftman, SAM, start-up.

Pengenalan Konsep Dasar Animasi 2D dan 3D kepada Anak SMA/SMK di Wilayah Provinsi Banten Suni, Eugenius Kau; Sutresno, Stephen Aprius; Sihombing, Denny Jean Cross; Christanto, Henoch Juli; Bata, Julius Victor Manuel; Airlangga, Gregorius; Setiawan, Yohanes Evan
Jurnal Pengabdian Masyarakat Charitas Vol. 3 No. 02 (2023): Jurnal Pengabdian Masyarakat Charitas Desember 2023
Publisher : Program Studi Teknik Industri, Fakultas Teknik, Universitas Katolik Indonesia Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/charitas.v3i02.4998

IGiven the rapid development of technology, education in the field of technology becomes crucial for everyone. One of the roles of technology education in fostering someone's creativity can be achieved through learning about things that require creativity, such as animation in the digital world. The Information Systems Program at Atma Jaya Catholic University of Indonesia organized a community service activity in the form of a workshop on November 25, 2023, covering the introduction to the basic concepts of 2D and 3D animation. The workshop was facilitated by Edwar Juanda, S.Ds., M.Ikom, as the speaker, and was attended by 90 participants from high schools and vocational schools in various areas around the Banten province. The workshop proceeded smoothly, and the participants appeared very enthusiastic and active throughout the event. Particularly during the workshop session, participants took turns trying out Virtual Reality tools provided by the speaker. The evaluation through a questionnaire received a positive response from participants with a score of 4.12, indicating agreement. It is hoped that this workshop will provide a broader insight into the younger generation regarding the application of technology, especially in the creation of 2D and 3D animations.

Pelatihan Pembuatan Konten Media Sosial Tiktok Bagi Pelajar SMA/SMK di Wilayah Provinsi Banten: Training on TikTok Social Media Content Creation for High School Students in the Province of Banten Sutresno, Stephen Aprius; Suni, Eugenius Kau; Bata, Julius Victor Manuel; Christanto, Henoch Juli; Airlangga, Gregorius; Sihombing, Denny Jean Cross; Philia, Gregoria
J-Dinamika : Jurnal Pengabdian Masyarakat Vol 9 No 2 (2024): Agustus
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/j-dinamika.v9i2.4651

Media Sosial Tiktok sangat melekat dengan keseharian generasi Z yang saat ini masih duduk di bangku sekolah. Tiktok terkadangan membuat anak-anak usia sekolah menghabiskan waktu secara berlebihan bermain gadget hingga mengganggu waktu belajarnya. Untuk meningkatkan produktivitas di media sosial, prodi Sistem Informasi Universitas Katolik Indonesia Atma Jaya menggelar pelatihan pembuatan konten Tiktok bagi pelajar SMA dan SMK di wilayah Tangerang, Banten. Narasumber pelatihan ini yang juga seorang influencer Tiktok berhasil meningkatkan pengetahuan dan keterampilan pelajar SMA dan SMK yang mengikuti pelatihan ini. Sebab pada proses pelatihannya peserta selain menerima penjelasan tentang materi berkonten, mereka juga langsung didampingi pada sesi praktek membuat konten Tiktok. Hasilnya, peserta semakin mampu membuat konten review kegiatan, review fasiltas dan tempat, dan konten informatif lainnya. Nilai pre-test dan post-test juga menunjukan bahwa terjadi peningkatan pengetahuan sebesar 74,4 persen pada peserta dalam pelatihan ini.

Forecasting Climate Change Impacts Using Machine Learning and Deep Learning: A Comparative Analysis Airlangga, Gregorius
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.v8i1.784

This study undertakes a comparative analysis of machine learning and deep learning models for forecasting the impacts of climate change, utilizing Cross-Validation Root Mean Squared Error (CV RMSE) to gauge performance. Analyzed models include Long Short-Term Memory (LSTM) networks (CV RMSE: 0.155), Linear Regression (CV RMSE: 5647815244.91), Random Forest (CV RMSE: 0.159), Gradient Boosting Machine (GBM) (CV RMSE: 0.164), Support Vector Regressor (SVR) (CV RMSE: 0.159), Decision Tree Regressor (CV RMSE: 0.199), and K-Nearest Neighbors (KNN) Regressor (CV RMSE: 0.166). The study rigorously processes climate change time series data to ensure accurate, generalizable results. LSTM networks demonstrated exceptional performance, indicating their strong capacity for modeling complex temporal sequences inherent in climate data, while Linear Regression lagged significantly behind, revealing limitations in addressing non-linear patterns of climate change. The promising results of Random Forest and SVR models suggest their applicability in environmental science forecasting tasks. Our findings offer valuable insights into the efficacy of various predictive models, aiding researchers and policymakers in leveraging advanced analytics for climate change mitigation strategies

Comparative Analysis of Machine Learning Models for Enhanced Chemical Detection in Sensor Array Data Airlangga, Gregorius
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.v8i1.785

The objective of this study was to compare the efficacy of various machine learning models for classifying chemical substances using sensor array data from a wind tunnel facility. Six widely recognized machine learning algorithms were assessed: Random Forest, Gradient Boosting, Logistic Regression, Support Vector Machine (SVM), Decision Tree, and K-Nearest Neighbors (KNN). The dataset, consisting of 288 sensor array features, was preprocessed and utilized to evaluate the models based on accuracy, precision, recall, and F1 score through a 5-fold cross-validation method. The results indicated that ensemble methods, particularly Random Forest and Gradient Boosting, outperformed other models, achieving an accuracy and F1 score of over 99%. KNN also demonstrated high efficacy with similar performance metrics. In contrast, Logistic Regression showed modest results in comparison. The study's outcomes suggest that ensemble machine learning models are highly suitable for chemical detection tasks, potentially contributing to advancements in environmental monitoring and public safety. The findings also highlight the importance of quality data preprocessing in achieving optimal model performance. Future research directions include exploring hybrid models, deep learning techniques, and assessing model robustness against environmental variabilities. This research underscores the transformative potential of machine learning in chemical detection and paves the way for developing more sophisticated and reliable detection systems.

Enhancing Medical Diagnostics with Ensemble Machine Learning: A Comparative Study of Gradient Boosting, XGBoost, LightGBM, and Blended Models Airlangga, Gregorius
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 2 (2024): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i2.842

This research investigates the performance of various machine learning models, including Gradient Boosting, AdaBoost, Support Vector Machine (SVM), Logistic Regression, XGBoost, LightGBM, and a Blended Model, in the context of medical diagnostics. The objective of the study is to identify the most accurate and reliable model for predicting outcomes, particularly in cases where the accurate identification of positive instances is critical. The research employs a systematic evaluation using cross-validation and test accuracy metrics to assess each model's performance. Results indicate that ensemble methods, such as Gradient Boosting, XGBoost, and LightGBM, generally outperform simpler models. LightGBM achieved the highest cross-validation accuracy at 89.10%, while the Blended Model demonstrated the potential of combining multiple classifiers, achieving a cross-validation accuracy of 90.19%. However, a common challenge across all models was balancing precision and recall for the positive class, suggesting the need for further optimization. The study concludes that while advanced ensemble methods show promise, enhancing the models' sensitivity to positive cases is crucial for improving their applicability in medical diagnostics. Future research should focus on refining these models to achieve a better balance between precision and recall, ensuring that critical cases are not overlooked.

Comparative Analysis of Machine Learning Models for Predicting Electric Vehicle Range Airlangga, Gregorius
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 1 (2024): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i1.740

This research presents a comprehensive analysis of various machine learning models to predict the electric range of electric vehicles (EVs). In the context of growing environmental concerns and the push for sustainable transportation, accurate prediction of EV range is crucial for consumer trust and wider adoption. We evaluated five different models: Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regressor, and Gradient Boosting Regressor, using a dataset that included a diverse array of EV attributes. The primary evaluation metric was the Mean Squared Error (MSE), applied both in cross-validation and on a test set. Our findings revealed significant differences in performance between linear models and ensemble methods. Linear models, while computationally efficient and interpretable, showed modest predictive capabilities, likely limited by their inability to capture complex, non-linear relationships in the data. Notably, Lasso Regression exhibited the highest error rates, possibly due to its feature exclusion in regularization. In contrast, ensemble methods, particularly the Random Forest Regressor and Gradient Boosting Regressor, demonstrated superior performance, effectively modeling non-linear relationships and intricate feature interactions. This study underscores the importance of model selection in predictive tasks, highlighting that more complex models, such as ensemble methods, are often more suitable for datasets with multifaceted interactions and non-linearities. The results of this research contribute to the evolving field of electric vehicle technology, providing insights that can guide future developments in EV range prediction, a key factor in the advancement of sustainable transportation. This research aids in understanding the application of machine learning in EV range prediction and lays the groundwork for future exploration, potentially incorporating real-time data and external factors for enhanced accuracy.

Deciphering Urban Happiness: Analysis of Machine Learning Approaches for Comprehensive Urban Planning Airlangga, Gregorius
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 1 (2024): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i1.741

This study embarks on an analytical journey to decode the complexities of urban happiness, leveraging a suite of advanced machine learning models. At the heart of our methodology is the innovative application of CatBoost, Random Forest, Gradient Boosting, and Linear Regression models, each chosen for its distinct ability to navigate the multifaceted nature of our urban dataset. CatBoost is highlighted for its proficiency in managing categorical data, essential in reflecting the diverse elements of urban environments. Concurrently, Random Forest's capability in reducing variance and overfitting, along with Gradient Boosting's precision in optimizing across various loss functions, plays a pivotal role in the accuracy of our predictions. Linear Regression serves as a baseline, offering simplicity and interpretability for comparative analysis. Central to our evaluation is the Root Mean Squared Error (RMSE) metric, providing a quantitative measure of our models' accuracy. This approach is instrumental in translating the intricate relationships within urban data into actionable insights for urban planning and policy-making. Our study not only demonstrates the effectiveness of an ensemble approach in machine learning but also emphasizes the importance of interpretability in model selection and evaluation. The findings offer a comprehensive understanding of urban happiness, serving as a valuable resource for stakeholders in urban development and policy formulation. This research marks a significant stride in harnessing machine learning's potential to enrich urban life quality.

Enhancing the Diagnosis of Gestational Diabetes Mellitus: A Comparative Study of Deep Learning and Traditional Machine Learning Models on Imbalanced Datasets Airlangga, Gregorius
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 2 (2024): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i2.841

This study aims to diagnose gestational diabetes mellitus (GDM) using advanced deep learning and traditional machine learning models, focusing on handling imbalanced datasets. The research objective is to develop models that can accurately classify GDM cases, which are often underrepresented in medical datasets. The methodology involves the use of both deep learning models, such as the Advanced Hybrid Model and Voting Model, and traditional machine learning models, including Random Forest, Gradient Boosting, and LightGBM. These models were trained on a balanced dataset achieved through the Synthetic Minority Over-sampling Technique (SMOTE) and evaluated using cross-validation and test accuracy metrics. The results indicated that the deep learning models achieved high cross-validation accuracy but faced challenges in classifying GDM cases on the test set, with lower precision and recall for the minority class. Traditional machine learning models also demonstrated strong performance but similarly struggled with sensitivity towards GDM cases. The study concludes that while these models show promise in diagnosing GDM, further refinement is necessary to improve their ability to handle imbalanced datasets. Future research should explore advanced techniques to enhance the detection of GDM cases, contributing to more reliable and accurate medical diagnostics.

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