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All Journal Conference SENATIK STT Adisutjipto Yogyakarta Angkasa: Jurnal Ilmiah Bidang Teknologi Compiler Quantum Teknika Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Vortex
Sri Mulyani
Institut Teknologi Dirgantara Adisutjipto
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Other

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Identifikasi Kerusakan Pada Landing Gear Pesawat Cessna C208B Sri Mulyani; Harliyus Agustian; Iqbal Dwi Anugerah Pulungan
Quantum Teknika : Jurnal Teknik Mesin Terapan Vol. 5 No. 2 (2024): April
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jqt.v5i2.20257

Abstract

One type of aircraft commonly used for flight training, patrol, and transportation in remote areas is the Cessna. It should be understood that each tool and component in the aircraft has its own level of importance and can experience failures in carrying out its functions. These failures often become a problem for users due to a limited understanding of the aircraft engine field. Issues arising from the aircraft's landing gear sometimes involve minor problems that do Does not require a high level of expertise. To solve this, it may be possible for someone with knowledge of landing gear to address the issue. By using the Case-Based Reasoning (CBR) method for fault identification, a technician's expertise in aircraft landing gear can be applied and integrated. The search for solutions or fault identification Can be obtained promptly In the testing results of the Fault System for Cessna C208B Aircraft Landing Gear using the CBR method, the system's calculations were consistent with the manual. However, the application still has some shortcomings in the system, such as the preprocessing stage, which has not been able to search for the root words in a question sentence.
Analisis Penggunaan Virtual Customer Assistant (VICA) Terhadap Efektivitas dan Efisiensi Layanan Penumpang di Yogyakarta International Airport (YIA) Sabdanti Dikatria Nofi Rizkia Sari; Gunawan Gunawan; Sri Mulyani; Buyung Junaidin; Elisabeth Anna Prattiwi
Vortex Vol 7, No 2 (2026)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/vortex.v7i2.4009

Abstract

Virtual Customer Assistant (VICA) adalah salah satu layanan customer yang disediakan oleh PT Angkasa Pura I di Yogyakarta International Airport (YIA), dalam upaya untuk meningkatkan excellent services dan meminimalisir penularan virus Covid-19. Penelitian ini bertujuan untuk menganalisis penggunaan Virtual Customer Assistant (VICA) terhadap efektivitas dan efisiensi layanan penumpang.Hal yang dianalisis adalah respon penumpang terhadap tingkat efektivitas dan efisiensi dengan metode kuantitatif deskriptif. Pengambilan data dengan menyebarkan kuesioner kepada 100 responden (penumpang), wawancara dan rekomendasi peneliti. Hasil pengumpulan data diolah menggunakan IBM Statistics SPSS 25 dan perhitungan manual untuk mencari persentase skor. Hasil analisis semua instrumen dinyatakan valid dan reliabel, tiap aspek varibel masuk dalam kategori >81,25%-100% sehingga dinyatakan Sangat Efektif dan atau Efisien. Rekomendasi layanan dengan menambahkan tempat dan disediakan layanan customer yang dikembangkan dengan Artificial Inteligence (AI).
Comparative Analysis of Statistical and Machine Learning Models for Air Passenger Demand Forecasting: Evidence from Domestic and International Passenger Segments Rianto Rianto; Sri Mulyani; Setia Wardani
Vortex Vol 7, No 2 (2026)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/vortex.v7i2.4015

Abstract

Accurate forecasting of air passenger demand is critical for aviation sector planning, yet model selection remains contested when data exhibit structural discontinuities and segment-specific behavior. This study benchmarks four forecasting approaches—SARIMA, SARIMAX, Prophet, and XGBoost—on monthly domestic and international passenger traffic data from 2019 to 2024, using a 12-month hold-out test set and a 36-month projection horizon. Model performance was evaluated using MAE, RMSE, and MAPE, with stationarity confirmed via Augmented Dickey-Fuller tests.Results reveal stark segmentation in model suitability. For international passengers, SARIMA achieved the lowest test MAPE (8.66%), substantially outperforming XGBoost (14.23%) and Prophet (57.65%), attributable to the strong first-order autocorrelation structure of post-recovery international traffic. For domestic passengers, SARIMA and SARIMAX both exhibited explosive forecast divergence (MAPE exceeding 11,900%), rendering them operationally unusable; XGBoost (MAPE 14.87%) emerged as the most reliable alternative by capturing non-linear interactions among lag features, rolling statistics, and annual trend components.The central finding concerns the role of exogenous variables: SARIMAX produced metrics identical to SARIMA for international passengers and marginally worse for domestic passengers, yielding no incremental predictive gain in either segment. This challenges the assumption that exogenous augmentation inherently improves seasonal time series models—such benefits are conditional on the relevance, stability, and non-redundancy of external regressors relative to existing seasonal components. These findings offer empirical guidance for segment-specific model selection in aviation demand forecasting and caution against uncritical adoption of SARIMAX when core seasonal structure is already sufficient.
Co-Authors Aryanti Dwi Puspita Astika Ayuningtyas Bangga Dirgantara Adiputra Buyung Junaidin, Buyung Dwi Hartini Elisabeth Anna Prattiwi Erni Jumiyanti Frencha Talantha Gunawan Gunawan Harliyus Agustian Iqbal Dwi Anugerah Pulungan Istyawan Priyahapsara Karseno KS Nurcahyani Dewi Retnowati Rahmawati, Fajar Khanif Rianto Rianto Rianto Rianto Rudi Setiawan Sabdanti Dikatria Nofi Rizkia Sari Setia Wardani