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All Journal JURNAL INFORMATIKA SAINTEK
Karina Imelda
Universitas Pelita Bangsa
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Other

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Optimalisasi Pengolahan Air Limbah Dengan Menggunakan Metode Ozon Microbubble Untuk Menurunkan Kadar COD (Chemical Oxygen Demand) Dan TSS (Total Suspended Solid) Di PT Industri Kimia Aji Susanto; Karina Imelda; Dodit Ardiatma; Nur Ilman Ilyas
Prosiding Sains dan Teknologi Vol. 1 No. 1 (2022): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 1 - Juli 2022
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

This study aims to determine the effect of variations in contact time on decreasing levels of Chemical Oxygen Demand (COD) and Total Suspended Solid (TSS) using Ozone Microbubble and determine the efficiency level of Ozone Microbubble. This research uses liquid waste from WWTP at PT. Chemical Industry Cikarang with variations in processing time of 20, 40, 60 minutes. The test results before the Ozone Microbubble method was applied did not meet the quality standards and after being applied with a time variation of 20, 40, 60 minutes, they met the Hyundai Industrial Estate wastewater quality standards. The decrease for COD levels with a time of 60 minutes was 614.6 mg/L. Meanwhile, the TSS level is 170.8 mg/L. Both still meet the quality standards of the Hyundai Industrial Estate. The efficiency level for COD is 33% and TSS is 32%. From the results of reducing COD and TSS, it can be concluded that the use of Ozone Microbubble can be used in chemical waste. In addition to the relatively high percentage of reduction, in terms of costs in this research, it is also relatively cheap and can be a proposed idea if it is to be implemented. Keywords: Ozon, Microbubble, Chemical Industry Liquid Waste, COD, TSS
Model Rekomendasi Konten Edukasi Diabetes pada Instagram dengan Integrasi TF-IDF dan Cosine Similarity berbasis Natural Language Processing Anggita Risqi Nur Clarita; Muhamad Fatchan; Karina Imelda
JURNAL INFORMATIKA Vol 15, No 1 (2026): Jurnal Informatika
Publisher : Informatics Engineering Department, Dayanu Ikhsanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55340/jiu.v15i1.2719

Abstract

Meningkatnya prevalensi Diabetes Melitus menuntut penguatan literasi kesehatan digital mandiri. Namun, mayoritas edukasi di Instagram bersifat generalis dan belum tersegmentasi sesuai karakteristik medis pasien. Penelitian ini bertujuan mengembangkan model rekomendasi konten edukasi diabetes personal menggunakan pendekatan Content-Based Filtering. Berbeda dari model pencocokan kata konvensional terdahulu yang gagal menangani tingginya tumpang tindih istilah medis pada teks non-formal, kebaruan penelitian ini terletak pada integrasi knowledge-base terminologi medis hierarkis (Tipe 1, Tipe 2, Umum) yang diselaraskan bersama praktisi kesehatan untuk memandu akurasi pembobotan. Metode yang digunakan meliputi Term Frequency-Inverse Document Frequency (TF-IDF) untuk representasi fitur dan Cosine Similarity untuk mengukur kemiripan leksikal antar-vektor teks. Hasil evaluasi terhadap 75 data uji menunjukkan capaian akurasi klasifikasi back-end sebesar 84% (Interval Kepercayaan 95%: [75,70%, 92,30%]), dengan kualitas urutan pemeringkatan rekomendasi terarah pada nilai rata-rata Precision at 3 (P@3) sebesar 88%. Analisis data empiris mengonfirmasi kesenjangan di lapangan di mana 72,45% konten didominasi materi Umum. Kesimpulannya, model ini menunjukkan potensi performa baik dalam meminimalkan bias prediksi pada dataset terkait, meski masih memiliki keterbatasan overlap kata kunci pada kategori Tipe 2 (Recall 0,76). Pengembangan ke depan memerlukan arsitektur multimodal untuk memproses informasi dari media visual.
Co-Authors Aji Susanto Anggita Risqi Nur Clarita Dodit Ardiatma Muhamad Fatchan Nur Ilman Ilyas