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.

Advanced Forecasting of Maize Production using SARIMAX Models: An Analytical Approach Airlangga, Gregorius
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.v8i1.7268

Agricultural production forecasting is crucial for food security and economic planning. This study conducts a detailed analysis of maize production forecasting using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, emphasizing the applicability of time-series models in capturing complex agricultural dynamics. Following a comprehensive literature review, the SARIMA model was justified for its ability to integrate seasonal fluctuations inherent in agricultural time series. Optimal model parameters were meticulously determined through an iterative process, optimizing the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The best-performing SARIMA(1, 1, 2)x(2, 2, 2, 12) model achieved an AIC of 339914.85450182937 and a BIC of 339950.64499813004, indicating its strong fit to the historical data. This model was applied to a historical dataset of maize production, providing forecasts that align closely with actual production trends on a short-term basis. Notably, the model's short-term predictions for the subsequent year showed less than a 2% deviation from the actual figures, affirming its precision. However, long-term forecasts revealed greater variability, underscoring the challenge of accounting for unforeseen environmental and economic factors in agricultural production systems. This research substantiates the efficacy of SARIMA models in agricultural forecasting, delivering strategic insights for resource management. It also points towards the integration of SARIMA with other variables and advanced modeling techniques as a future avenue to enhance forecasting robustness, particularly for long-term projections. The findings serve as a valuable resource for policymakers and stakeholders in optimizing decision-making processes for agricultural production.

Integrating LightGBM and XGBoost for Software Defect Classification Problem Airlangga, Gregorius
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.v8i1.7267

Software defect classification is a crucial process in quality assurance, pivotal for the development of reliable software systems. This paper presents an innovative approach that synergizes traditional software complexity metrics with advanced machine learning algorithms, namely Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost), to enhance the accuracy and efficiency of software defect classification. Leveraging a dataset characterized by McCabe's and Halstead's metrics, this study embarks on meticulous data preprocessing, feature engineering, and hyperparameter optimization to train and evaluate the proposed models. The LightGBM and XGBoost models are fine-tuned through the Optuna framework, aiming to maximize the ROC-AUC score as a measure of classification performance. The results indicate that both models perform robustly, with XGBoost demonstrating a slight superiority in predictive capability. The integration of machine learning with traditional complexity metrics not only enhances the defect classification process but also provides deeper insights into the factors influencing software quality. The findings suggest that such hybrid approaches can significantly contribute to the predictive analytics tools available to software engineers and quality assurance professionals. This research contributes to the field by offering a comprehensive methodological framework and empirical evidence for the effectiveness of combining machine learning algorithms with traditional software complexity metrics in software defect classification.

Comparison of Predictive Modeling Concrete Compressive Strength with Machine Learning Approaches Airlangga, Gregorius
UKaRsT Vol. 8 No. 1 (2024): APRIL
Publisher : Kadiri University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30737/ukarst.v8i1.5532

Accurately predicting concrete compressive strength is fundamental for optimizing mix designs, ensuring structural integrity, and advancing sustainable construction practices. Increased demands for safer, more durable infrastructure necessitate effective predictive concrete compressive strength models. This research aims to compare the effectiveness of six machine learning models such as Linear Regression, Random Forest, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting, and XGBoost to predict concrete compressive strength. Used a dataset of 1030 instances with varying mixture compositions, conducted extensive exploratory data analysis, and applied feature engineering and data scaling to enhance model performance. Assessments were performed with a 5-fold cross-validation approach with the R-squared (R²) metric. In addition, the SHAP value is used to understand the influence of each feature on the compressive strength results. The results revealed that XGBoost significantly outperformed other models, achieving an average R² value of 0.9178 with a standard deviation of 0.0296. Notably, Random Forest and Gradient Boosting also demonstrated robust capabilities. Based on our experiment, these models effectively predicted compressive strengths close to actual measured values, confirming their practical applicability in civil engineering. SHAP values provided insights into the significant impact of age and cement quantity on model outputs. These results highlight the advanced ensemble methods' effectiveness in concrete compressive strength prediction and underscore the importance of feature engineering in enhancing model accuracy.

Exoplanet Classification Through Machine Learning: A Comparative Analysis of Algorithms Using Kepler Data Airlangga, Gregorius
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1303

This study delves into the classification of exoplanets using data from the Kepler Space Telescope, comparing a suite of machine learning (ML) models to ascertain their efficacy in distinguishing confirmed planets, candidates, and false positives. With a dataset meticulously preprocessed for quality, completeness, and relevance, we embarked on an analytical journey employing models like Decision Tree, Random Forest, Hist Gradient Boosting, CatBoost, AdaBoost, LightGBM, XGBoost, Extra Trees, Logistic Regression, and XGBoost RF. These models underwent rigorous evaluation across metrics such as Accuracy, Precision, Recall, and F1 Score, revealing an unprecedented level of performance. Our findings showcased a near-uniform perfection in model predictions, with scores touching the zenith of 1.0 across most metrics for the majority of models, indicating their flawless prediction capabilities. This remarkable performance, however, was nuanced by the Gaussian NB model's slightly less than perfect scores of 0.99, highlighting a minor deviation due to its probabilistic nature. While these results underscore the models' exceptional accuracy and reliability in classifying exoplanetary data, they also prompt a critical examination of potential overfitting, the dataset's complexity, and the models' generalizability to unseen data.

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