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All Journal Jurnal Penelitian Teknologi Informasi dan Sains Sevaka: Hasil Kegiatan Layanan Masyarakat
Ika Ima Nissa
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Engineering Health Professions

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Penyuluhan Klasifikasi Gejala Keterlambatan Bicara (Speech Delay) Pada Anak Menggunakan Algoritma Naive Bayes, C4.5, Dan K-Nerest Neighbor (K-NN) Putri Ramadani; Ika Ima Nissa; Nur Indah Nasution; Baginda Restu Al Ghazali
Sevaka : Hasil Kegiatan Layanan Masyarakat Vol. 2 No. 2 (2024): Mei : Sevaka : Hasil Kegiatan Layanan Masyarakat
Publisher : STIKES Columbia Asia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62027/sevaka.v2i2.534

Abstract

Speech delay in children is a developmental issue commonly encountered in society, which can affect various aspects of a child's life, including communication, social interaction, and academic development. Early detection of speech delay is crucial for providing appropriate interventions to minimize its long-term impact on the child. This study aims to introduce the use of machine learning algorithms in detecting speech delay symptoms in children. Three machine learning algorithms applied in this study are Naïve Bayes, C4.5, and K-Nearest Neighbor (K-NN). These algorithms are used to classify speech delay symptoms based on health data, medical history, and environmental factors such as speaking habits and eating patterns. The outreach was conducted at Puskesmas Kota Rantauprapat with the involvement of parents and healthcare providers as participants. The experimental results showed that all three algorithms performed well in terms of accuracy, though with varying error rates. Naïve Bayes achieved relatively high accuracy but had a higher false positive rate compared to C4.5 and K-NN. C4.5 provided more stable results and was easier to interpret due to its decision tree structure. Meanwhile, K-NN performed better with data that had irregular distribution. This outreach is expected to assist both the community and healthcare providers in early detection of speech delay in children, providing a more efficient and affordable means for early intervention, which ultimately leads to better outcomes for children with speech delay.
Implementasi Metode TOPSIS dalam Sistem Pendukung Keputusan Pemilihan Kedelai Terbaik untuk Produksi Tempe Ika Ima Nissa; Widyawati Widyawati
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 4 No. 2 (2026): Juni: JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS (JPTIS)
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v4i2.4189

Abstract

One of the most significant foods for the people of Indonesia, tempeh is made mostly from soybeans. Soybean quality selection is still mostly done by hand, relying on eye inspection and the expertise of business actors. This may cause subjective evaluations, lengthy decision-making processes, and uneven outcomes. With the TOPSIS technique as its foundation, this project intends to construct a Decision Support System (DSS) for selecting high-quality soybeans. Color, texture, price, scent, and flavor are some of the evaluation criteria. Observations and interviews were conducted in Rumah Tempe A-Zaki Padang to gather data for the research. Ranking the options according to how close they were to the positive ideal solution and how far away from the negative ideal solution was done using the TOPSIS approach. In terms of preference values, the Green Soybean option came out on top with 0.9692, followed by Black Soybeans with 0.2335, Yellow Soybeans with 0.1516, and Brown Soybeans with 0.1181. Based on these outcomes, it seems that TOPSIS can indeed provide objective suggestions for soybean selection according to a number of different standards. Business players in the tempeh industry may benefit from this decision support system by using it to choose high-quality soybean raw materials more efficiently and accurately.
Co-Authors Baginda Restu Al Ghazali Nur Indah Nasution Putri Ramadani Widyawati Widyawati