Sistem Informasi - Institut Teknologi Nasional Bandung

Akreditasi BAIK SEKALI (VERY GOOD)

Sekilas Sistem Informasi

Program Studi Sistem Informasi Itenas mengintegrasikan teknologi informasi dengan dunia bisnis dan manajemen. Mahasiswa dibekali kompetensi dalam analisis sistem, pengembangan perangkat lunak, manajemen data, dan pemanfaatan teknologi informasi untuk mendukung pengambilan keputusan. Kurikulum dirancang mengikuti kebutuhan industri digital dan perkembangan teknologi terkini. Lulusan siap berkarier sebagai system analyst, IT consultant, data analyst, atau entrepreneur di bidang teknologi informasi. Program Studi Sistem Informasi Itenas memiliki fasilitas pembelajaran yang lengkap, suasana akademik yang nyaman, serta dosen yang berpengalaman dan kompeten pada bidangnya.

Program Studi Sistem Informasi Itenas telah menerapkan kurikulum berbasis Outcome-Based Education (OBE). Program Studi Sistem Informasi Itenas mengintegrasikan pembelajaran berbasis proyek (project-based learning), kolaborasi industri, serta peluang partisipasi dalam program Merdeka Belajar Kampus Merdeka (MBKM), sehingga mampu menjawab tantangan bukan hanya pada revolusi industri 4.0 yang berfokus pada:

Otomatisasi

Internet of Things (IoT)

Big data

Artificial Intelligence (AI)

Cyber-physical systems,

Tetapi juga mulai menjawab tantangan revolusi industri 5.0 yang menekankan pada:

Kolaborasi antara manusia dan mesin cerdas

Personalisasi produk dan layanan

Keberlanjutan dan tanggung jawab sosial, dalam mewujudkan transformasi digital nasional.

Bidang-bidang kajian yang menjadi arah riset program Studi Sistem informasi Itenas adalah “Sistem Informasi Hijau” yang dapat digambarkan sebagai berikut:

Mengapa Sistem Informasi Itenas ?

Lulusan Program Studi Sistem Informasi Itenas dapat diserap oleh hampir semua bidang
Mulai dari bidang perbankan, kesehatan, manufaktur dan industri, instansi pemerintah, pariwisata, dan banyak lagi.

Kurikulum Program Studi Sistem Informasi Itenas dirancang responsif terhadap kebutuhan industri digital
Membekali mahasiswa dengan keahlian dalam analisis sistem, pengembangan aplikasi, manajemen data, dan pemanfaatan teknologi informasi yang memfasilitasi kebutuhan dunia usaha dan industri (DUDI).

Sarana dan prasarana yang mendukung
Didukung oleh dosen professional yang tersertifikasi, fasilitas laboratorium modern yang didukung oleh High Performance Computer (HPC), dan kolaborasi dengan industri sehingga mahasiswa mendapatkan pengalaman praktis, kesempatan magang, riset terapan, dan pengabdian kepada masyarakat.

**Etika dan tata krama dalam interaksi civitas academica Sistem Informasi Itenas
Budaya yang menjujung tinggi nilai-nilai kesopanan dan tata krama yang baik dalam interaksi antara para civitas academica (mahasiswa, dosen, dan tenaga penunjang) di Progam Studi Sitem Informasi Itenas.

Progam Studi Sitem Informasi Itenas menanamkan jiwa kewirausahaan digital**
Mendorong mahasiswa menjadi inovator dan pelaku industri TI. Lulusannya terbukti kompeten, adaptif, dan siap bersaing di pasar kerja nasional maupun global.

Prospek Karier

Mahasiswa Berprestasi

Alumni

Penelitian dan PKM Unggulan

TRIPLE EXPONENTIAL SMOOTHING AND RANDOM FOREST ALGORITHMS FOR A HYBRID RISK FORECASTING BY USING WEIGHTED EVENT DATA

R. Budiraharjo, Mira Musrini B., Carissa Adnyana Putri R.

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TRIPLE EXPONENTIAL SMOOTHING AND RANDOM FOREST ALGORITHMS FOR A HYBRID RISK FORECASTING BY USING WEIGHTED EVENT DATA

R. Budiraharjo, Mira Musrini B., Carissa Adnyana Putri R.

Risk forecasting plays a crucial role in various fields, ranging from finance to environmental management. This research proposes a novel approach that combines the Triple Exponential Smoothing (TES) method and the Random Forest (RF) algorithm to achieve more accurate and reliable risk forecasting. TES is highly effective in predicting seasonality and trends in time-series data, while RF excels in handling complex non-linear relationships and mitigating model overfitting. By integrating these two methods, the proposed hybrid model aims to leverage the strengths of each method to enhance the accuracy and performance of risk prediction. This research represents an effort to improve the performance of the seventh step in the Improved Multi-Attribute Risk Assessment (IMARA) method, which is risk forecasting. The experiments in this study utilize a dataset from PT Terminal Peti Kemas Surabaya to evaluate the performance of the proposed approach. The results of this research are expected to demonstrate an improvement in the accuracy and performance of risk prediction compared to the risk prediction performance generated by the TES and RF methods when applied individually, especially in dealing with complex data patterns and dynamic risk factors. This research uses three scenarios for combining TES and RF to obtain the highest accuracy in risk forecasting. The results from the first phase of the experiments showed that Scenario 3 yields slightly better accuracy compared to the two other scenarios.

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AI-BASED AUTOMATED ASSESSMENT FOR ENGLISH SPEAKING PROFICIENCY

Nur Fitrianti F., S.Kom., M.T, Corry Caromawati, S.S., M.A., Ph.D., Sofia Umaroh, S.Pd.,M.T. Wahyudi

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AI-BASED AUTOMATED ASSESSMENT FOR ENGLISH SPEAKING PROFICIENCY

Nur Fitrianti F., S.Kom., M.T, Corry Caromawati, S.S., M.A., Ph.D., Sofia Umaroh, S.Pd.,M.T. Wahyudi

Kemampuan berkomunikasi dalam bahasa Inggris yang masih rendah di Indonesia (peringkat 80 dalam English Proficiency Index 2024) disebabkan oleh faktor kompleks, termasuk metode pembelajaran tradisional yang kurang melatih penggunaan aktif bahasa, ketimpangan kualitas pendidikan, dan penilaian (assessment) yang tidak autentik. Tes konvensional sering gagal mengukur language proficiency secara holistik (akurasi, kompleksitas, kefasihan) sesuai standar CEFR, serta menghadapi masalah rater fatigue dan bias. Meskipun tes berbasis komputer seperti TOEFL iBT atau Duolingo tersedia, harganya mahal dan tidak menyediakan umpan balik pembelajaran.

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NEPTUNE: kNowledge rEPresentaTion Using kNowlEdge Graph and Ontology

Sofia Umaroh, Kurnia Ramadhan Putra, Muhammad Hilmy Aiman, Bennart Dem Gunawaan

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NEPTUNE: kNowledge rEPresentaTion Using kNowlEdge Graph and Ontology

Sofia Umaroh, Kurnia Ramadhan Putra, Muhammad Hilmy Aiman, Bennart Dem Gunawaan

Research Question RQ1: How to build ontology in the domain of researcher profiles to gain knowledge of potential research collaboration in higher educationRQ2: How to represent knowledge in the form of a knowledge graph based on the ontology of the researcher profile domain Contribution Practices:Providing knowledge about the profile of researchers that are in accordance with a particular scientific field based on research history, publications, projects, funding, departments/study programs, scientific groups as considerations for research collaboration within and across scientific fields. Potential for increasing research collaboration between lecturersTheory: Contribution of utilizing ontology in building Knowledge Graph to represent knowledge related to researcher profiles for consideration of research collaboratio. MethodologyProposed Method Ontology and Evaluation Using SPARQL Transformation into a Knowledge Graph Recommendation using Link Prediction Method ResultsKnowledge Graph EvaluationCompetency Question: How many publications that :hasPublished together with researcher in ItenasCompetency Question: How is overall research collaboration representation between reseracher in Itenas IntroductionThe needs for more structured and effective ways to represent the profiles of researchers (Huynh, 2014). Research collaboration has become increasingly vital for higher education institutions (Lima, et. al., 2021; Bravo, 2019). Identify potential collaborators is difficult especially for young researchers (Bravo, 2019). Proposed an ontology based model using semantic language of Web Ontology Language (OWL) enriched by transforming the populated ontology into a knowledge graph using Neo4J. Predict potential collaborations between researchers using content-based and graph-based similarity measures. Recommendation Model Content-Based Similarity: hasSubject The similarity calculation based on hasSubject content is done by computing the TF-IDF property of hasSubject for each Person and then calculating the Cosine Similarity. Graph-Based Similarity: Link-Prediction Network embedding using the Node2Vec method with Random walk algorithm to produce embedding vectors based on the network structure where researchers have close relationships in the network.

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