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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Pendidikan dan Teknologi Indonesia
Sumihar, Yo'el Pieter
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Computer Science & IT Education Engineering

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AUTOMATED ANAEMIA DETECTION FROM CONJUNCTIVA IMAGES : A MACHINE LEARNING APPROACH FOR ANDROID APPLICATION Henry, Valentino Sas; Sumihar, Yo'el Pieter; Maedjaja, Febe
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 2 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i2.5218

Abstract

Anaemia is a common global disorder condition in which the red blood cell count is lower than normal. Traditional diagnostic methods often prove costly, invasive, and inaccessible, leading to delays in treatment and severe consequences. This study explores non-invasive techniques leveraging smartphone technology for efficient anaemia detection. Some researcher investigated that eye’s conjunctiva analysis can a viable alternative, considering its rich blood vessel network and sensitivity to anaemia indicators, independent of skin color. Utilizing smartphone cameras, the study establishes a robust correlation between the color of the conjunctiva and anaemia status, offering a promising avenue for non-invasive diagnosis. Employing a hybrid methodology, the study integrates You Only Look Once (YOLO) version 8 for efficient object detection, achieving a mean average precision of 96% in extracting Regions of Interest (ROI) from conjunctiva images. Subsequently, K-Nearest Neighbors (KNN) classification of features extracted from these ROI’s attained an 83% accuracy rate in determining anaemia status. Furthermore, the study aims to extend its impact by developing an Android application using the Flutter framework. This application integrates the established YOLO and KNN approaches, enabling real-time anaemia detection through smartphone cameras. Such a tool holds the potential to facilitate early evaluations in resource-constrained regions, enabling timely diagnosis and intervention, thus mitigating the adverse effects of untreated anaemia.
Implementasi Metode ViSQOL Dalam Mengidentifikasi Noise pada Kualitas Suara Streaming Spotify Setiawan Matangkin, Jimmi; Rudatyo Himamunanto, Agustinus; Budiati, Haeni; Sumihar, Yo'el Pieter
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 7 (2025): JPTI - Juli 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.848

Abstract

Kualitas suara pada layanan streaming Spotify seringkali tidak konsisten akibat gangguan noise dan variasi parameter jaringan, yang berdampak pada kualitas pengalaman pengguna (QoE). Penelitian ini bertujuan mengevaluasi kualitas audio Spotify menggunakan algoritma ViSQOL dengan menganalisis pengaruh jenis noise seperti pink noise, background noise, compression noise, dan impulse noise. Network noise juga diuji berdasarkan parameter jaringan yaitu throughput, delay, packet loss, dan jitter. Sebanyak 800 sampel audio direkam menggunakan Audacity dan dianalisis di MATLAB untuk memperoleh nilai Mean Opinion Score (MOS), Signal-to-Noise Ratio (SNR), dan Spectral Distortion. Hasil menunjukkan bahwa pink noise 50% menurunkan MOS menjadi 61–65%, sementara impulse noise memberikan dampak paling signifikan dengan MOS 15–17%. Background noise masih dapat ditoleransi. Pada parameter jaringan, MOS tertinggi diangka 4.31 terjadi pada delay 132.16 ms dan packet loss 0.49%, sedangkan MOS terendah diangka 4.26 tercatat saat delay 62.15 ms dan packet loss 1.9%. Temuan ini menegaskan pentingnya pengendalian terhadap noise dan stabilitas jaringan untuk menjaga kualitas layanan audio.
Co-Authors Febe Maedjaja Haeni Budiati Henry, Valentino Sas Rudatyo Himamunanto, Agustinus Setiawan Matangkin, Jimmi