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INTEGER: Journal of Information Technology
Published by Institut Teknologi Adhi Tama Surabaya
ISSN : 2579566X     EISSN : 2579566X     DOI : -
Core Subject : Science,
Computer Science & IT
This journal contains articles from the results of scientific research on problems in the field of Informatics, Information Systems, Computer Systems, Multimedia, Network and other research results related to these fields.
Arjuna Subject : -
Articles 265 Documents
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Ad-Hoc Business Intelligence for Agile Decision-Making: A Case Study Using Adventure Works 2022 Maulidati, Zuli; Pangestu, Satrio Bagas; Aini, Salma Nur
INTEGER: Journal of Information Technology Vol 10, No 1: April 2025
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v10i1.7537

Abstract

Agile decision-making in today's business is highly crucial for an organisation to stay competitive. Business intelligence has overcome the situation to provide solutions in any crucial condition. Unlike the static dashboard, the ad hoc dashboard is considered to be a viable solution in tailoring business to make a data-driven decision with a high flexibility. This study aims to examine and implement ad-hoc dashboards using the Adventure Works 2022 dataset. The result of the study shows that both dashboards implement interactive filtering, drill-down capabilities, and real-time visualization to enhance data-driven decision-making agility. The design of dashboard A provides a structured, multi-dimensional view suitable for in-depth analysis; meanwhile, dashboard B prioritises simplicity and accessibility, presenting key insights intuitively for quick decision-making. Furthermore, the study highlights the result that the biggest challenges of building a dashboard are information overload and usability. It is important to note that the implementation of ad-hoc BI should balance between analytical capabilities and user-friendliness to ensure that dashboards provide meaningful insights and are easy to use by end users. Keywords: Ad-hoc BI, Agile Decision Making, BI Dashboard
Deteksi PCOS pada Wanita Menggunakan Explanatory Data Analysis (EDA) dan Support Vector Machine (SVM) Rahmawati, Weny Mistarika; Edelani, Renovita
INTEGER: Journal of Information Technology Vol 10, No 1: April 2025
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v10i1.7595

Abstract

Polycistic Ovarium Syndrom (PCOS) merupakan gangguan hormonal yang terjadi pada Wanita usia produktif dan bisa mengakibatkan infertilitas. PCOS sendiri belum diketahui penyebab pastinya tetapi factor genetic dan pola hidup merupakan factor yang dapat memengaruhi seorang Wanita terkena PCOS. Penelitian ini bertujuan melakukan deteksi PCOS berdasarkan data yang terdiri dari umur, indeks masa tubuh, level testosterone serta jumlah folikel. Data awal yang didapat memiliki distribusi yang tidak baik atau bisa dikatakan tidak seimbang. Peneliti melakukan Explanatory Data Analysis (EDA) pada tahap awal dengan membuat scatterplot untuk mencari korelasi setiap fitur dengan kelas target. Hasilnya ada fitur keteraturan haid yang nilai kealpaannya sangat mempengaruhi deteksi PCOS sehingga dilakukan penghapusan data  pada nilai null pada fitur tersebut. Setelah itu dilakukan klasifikasi Support Vector Machine (SVM) untuk memisahkan kelas terdiagnosa PCOS atau tidak. Beberapa kernel SVM diujikan untuk mengetahui hasil terbaik yang bisa dihasilkan. Evaluasi dilakukan dengan menghitung akurasi, precision, recall dan f1-score pada confussion matrix yang terbentuk. Hasil dari penelitian menunjukkan bawa kernel polynomial memberikan hasil klasifikasi terbaik dengan akurasi sebesar 89,62%, precision 81,08%, recall 88,23% dan f1-score 84,5%. Penelitian ini mengonfirmasi bahwa kombinasi EDA dan SVM dapat digunakan sebagai pendekatan yang efektif dalam mendukung deteksi PCOS.
Media Pembelajaran Berbasis Game Edukasi Aksara Jawa Murda Menggunakan Algoritma Fisher Yates Shuffle Maulana, Fery; Alamsyah, Muslim; Anggadimas, Nanda Martyan
INTEGER: Journal of Information Technology Vol 10, No 1: April 2025
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v10i1.7266

Abstract

Pengembangan media pembelajaran berbasis game edukasi semakin dibutuhkan untuk mendukung proses pembelajaran yang interaktif dan menarik, khususnya dalam mata pelajaran Bahasa Jawa. Penelitian ini bertujuan untuk mengembangkan game edukasi aksara Jawa Murda untuk siswa kelas VII SMP dengan menggunakan algoritma Fisher-Yates Shuffle. Algoritma ini digunakan untuk mengacak soal, sehingga memberikan pengalaman bermain yang dinamis dan tidak monoton. Game yang dikembangkan berbentuk permainan petualangan dengan lima level, di mana setiap level menyajikan soal pilihan ganda terkait aksara Jawa Murda. Setelah menyelesaikan lima level, pemain akan kembali ke menu utama, dan set soal kedua akan ditampilkan pada sesi permainan berikutnya. Hasil penelitian menunjukkan bahwa game ini efektif meningkatkan minat belajar siswa terhadap materi aksara Jawa Murda. Uji coba dilakukan pada siswa kelas VII dengan hasil yang menunjukkan tingkat kepuasan tinggi terhadap aspek visual, fungsionalitas, dan efektivitas pembelajaran. Dengan adanya media pembelajaran berbasis game ini, diharapkan siswa dapat belajar aksara Jawa Murda dengan lebih menyenangkan dan efisien.Kata Kunci: Media pembelajaran, game edukasi, aksara Jawa Murda, Fisher-Yates Shuffle, siswa SMP.
Penerapan Knowledge-based system untuk Identifikasi Penyakit Pencernaan dan Pernapasan Manusia Prasetya, Hafid Arjul; Riska, Suastika Yulia
INTEGER: Journal of Information Technology Vol 10, No 1: April 2025
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v10i1.7580

Abstract

Digestive and respiratory diseases are often considered common, but if left untreated, they can lead to death. Lack of public awareness regarding the importance of medical consultation and limited operational time of health services cause many individuals to self-diagnose diseases. This research aims to develop a knowledge-based system to diagnose digestive and respiratory diseases in humans. This system is expected to provide accurate and relevant diagnosis solutions, as well as support the prevention and early treatment of these diseases. This research includes 8 types of diseases analyzed along with 29 symptoms. The process started with identifying the problem area and determining the decision target based on the data of 8 diseases, followed by the creation of a dependency diagram. Next, IF-THEN rules were developed, and after the rules were formed, the next step was to structure the Backward Chaining and Certainty factor process. This process resulted in the conclusion of the diagnosis of digestive and respiratory diseases. During system testing, the diagnosis results are compared with the expert's knowledge. This test aims to ensure a match between the system results and expert knowledge and to test the accuracy of the data obtained. Based on the results of testing 10 samples of processed data, the system showed an accuracy rate of 100%, which proves that this knowledge-based system works well and in accordance with expert knowledge.
Analisis Perbandingan Algoritma Decision Tree, Random Forest, dan XGBoost untuk Klasifikasi Penyakit Infeksi Gigi dan Mulut Seno Aji, Bernadus Anggo; Setiawan, Yohanes; Anggraini, Sukma Dewi
INTEGER: Journal of Information Technology Vol 10, No 1: April 2025
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v10i1.7501

Abstract

Oral and dental health are indicators of overall body health. Several factors contribute to dental and oral diseases, such as smoking, alcohol consumption, and excessive intake of sugary foods. Untreated dental diseases can lead to dental and oral infections. These infections may cause various complications, making proper treatment essential. This study aims to develop a classification model for dental and oral infections to assist in early diagnosis. The methods used in this research are tree-based algorithms, including Decision Tree, Random Forest, and XGBoost. Tree-based methods are among the algorithms suitable for categorical input data. The classification results using these methods achieved accuracies of 87.5%, 91.7% and 93.1% without SMOTE and 88.9%, 93.1% and 97.2%.. with SMOTE for handling class imbalance. The best-performing model in this study is XGBoost with SMOTE-applied training data.

Page 27 of 27 | Total Record : 265

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