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All Journal JUITA : Jurnal Informatika JIEET (Journal of Information Engineering and Educational Technology) ILKOM Jurnal Ilmiah Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal GERVASI: Jurnal Pengabdian kepada Masyarakat Jurnal Informatika Global bit-Tech Jurnal Abdimas Mandiri Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer Networks, Architecture and High Performance Computing Jurnal Generic RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Jurnal Pengembangan Sistem Informasi dan Informatika Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal INFOTEL Jurnal Software Engineering and Computational Intelligence Journal of Intelligent Networks and IoT Global Impression : Jurnal Teknologi dan Informasi SISFOTENIKA Jurnal Ilmu Teknologi Informasi Indonesia (JITIFNA)
Nazori Suhandi
Fakultas Ilmu Komputer, Universitas Indo Global Mandiri
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Journal : bit-Tech

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Klasifikasi Penyakit TBC Menggunakan Metode UMAP dan K-NN Nazori Suhandi; Rendra Gustriansyah; Abel Destria
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2227

Abstract

Tuberkulosis (TBC) adalah penyakit infeksi yang disebabkan oleh bakteri Mycobacterium tuberculosis, yang dapat menyebar dengan cepat melalui udara. Deteksi dini yang akurat sangat penting dalam penanganan penyakit ini untuk mencegah penyebaran lebih lanjut serta meningkatkan efektivitas pengobatan. Diagnosis yang tidak tepat dapat menyebabkan keterlambatan dalam pengobatan, sehingga meningkatkan risiko komplikasi serius bagi pasien. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem klasifikasi TBC menggunakan metode Uniform Manifold Approximation and Projection (UMAP) dan K-Nearest Neighbors (K-NN) di Puskesmas Prabumulih Timur. Dataset yang digunakan terdiri dari 278 data pasien dengan berbagai atribut klinis terkait gejala TBC. Proses analisis diawali dengan tahap pra-pemrosesan data, termasuk penghapusan data duplikat, encoding data kategorikal, serta penanganan nilai yang hilang. Untuk meningkatkan akurasi klasifikasi, metode Elbow diterapkan guna menentukan nilai K optimal, dengan hasil terbaik pada K=3. Data kemudian dibagi menjadi 80% data pelatihan dan 20% data uji guna menghindari overfitting dan meningkatkan reliabilitas model. Pengujian dilakukan dengan membandingkan dua skenario, yaitu K-NN tanpa UMAP dan K-NN dengan UMAP. Hasil evaluasi menggunakan Confusion Matrix menunjukkan bahwa penerapan UMAP meningkatkan accuracy dari 93,48% menjadi 100%, dengan precision dan recall juga mencapai nilai maksimal. Penelitian ini berkontribusi dalam pengembangan sistem klasifikasi berbasis machine learning yang lebih akurat dan efisien untuk membantu tenaga medis dalam mendiagnosis TBC secara cepat, tepat, dan optimal dalam sistem layanan kesehatan.
Decision Support System For Selecting Smart Indonesia Card Candidates Using Preference Selection Index Method M. Reza Fhalepi; Herri Setiawan; Nazori Suhandi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3068

Abstract

The Kartu Indonesia Pintar (KIP) program is a government initiative designed to ensure equal access to higher education for students from low-income families. However, the selection process remains challenging due to the large number of applicants, diverse evaluation criteria, and reliance on manual judgment, which can lead to inefficiency and bias. This study develops a decision support system (DSS) using the Preference Selection Index (PSI) method to improve transparency and objectivity in selecting KIP recipients at Universitas Indo Global Mandiri. Data were obtained through observation, structured interviews, documentation, and secondary records from the BKABK finance division. Five main criteria were used in the evaluation process: parents’ occupation, housing condition, number of siblings, academic achievements, and interview performance. The PSI method was implemented through data normalization, calculation of mean and deviation, automatic weight generation, and computation of each applicant’s final PSI score. A total of 270 valid applicants were processed, with most achieving scores between 0.80 and 0.90 (mean = 0.86; SD = 0.04), reflecting a high level of competition. The top five candidates scored between 0.8787 and 0.9179, led by Christopher Nathan Tanugraha and Kiagus Deru Cahyadi. These results demonstrate that PSI can reduce subjectivity in weight assignment, increase efficiency, and minimize human error, while ensuring fair scholarship distribution. More broadly, the proposed PSI-based DSS can be applied in other universities and scholarship programs, offering a scalable solution for equitable and data-driven decision-making in higher education.
Co-Authors Abel Destria Ahmad Sanmorino Amalia, Marshanda Antony, Fery Apri Aidila Utami Arda Damayanti Ariesta Diana Putri Damayanti, Arda Destria, Abel DEWI SARTIKA Efri Ayu Kartika Putri Fery Antony Hastha Sunardi Herri Setiawan Imelda Saluza, Imelda Indah Charista Irma Yuliawati Jhons Fransdesker John Roni Coyanda Juhaini Alie Kanaka, Nayaka Al Syahreal Kris, Via M. Reza Fhalepi Masda Rahadian Muhammad Haviz Irfani Putri, Alfina Elsa Putri, Meilany Aulia Rachmansyah Rachmansyah Ramadhani, Kiki Ramadhanti, Nur Fadhilah Ratna Meilinda Rendra Gustriansyah Rendra Gustriansyah Sari Agnisa Sartika, Dewi Shinta Puspasari Shinta Puspasari Shinta Puspasari Sri Primaini Sunardi, Hastha Sunardi, Hastha Suzan Agustri Wiyanto, Ari