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All Journal JSAI (Journal Scientific and Applied Informatics) Jurnal Abdimas Mandiri
Agustina Heryati
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Computer Science & IT Education Social Sciences

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PELATIHAN PENULISAN KARYA ILMIAH BAGI GURU DI LINGKUNGAN DINAS PENDIDIKAN KOTA PALEMBANG Fauzia Afriyani; Abdul Kholik; Agustina Heryati; Try Wulandari; Dio Resta Permana
Jurnal Abdimas Mandiri Vol. 7 No. 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jam.v7i1.2971

Abstract

Dinas pendidikan Kota Palembang membuat pelatihan penulisan karya ilmiah bagi guru yang bertujuan untuk meningkatkan kemampuan menulis para guru dalam menghasilkan karya ilmiah yang berkualitas dan bermanfaat bagi dunia pendidikan. Pelatihan tersebut meliputi berbagai aspek penulisan karya ilmiah, seperti pemilihan topik penelitian, perumusan masalah penelitian, pengumpulan dan analisis data, serta penyusunan laporan penelitian. Metode dalam pelatihan ini meliputi Identifikasi Tujuan, Persiapan Materi, Penentuan Jadwal dan Durasi, Pemilihan Peserta, Sesi Praktikum, Umpan Balik dan Evaluasi. Dari survey sebelum pelatihan Peserta kesulitan dalam menyusun desain penelitian yang sesuai dan memilih metode analisis yang tepat. Peserta seringkali tidak yakin bagaimana menganalisis data dan menginterpretasikan hasilnya. Peserta seringkali mengalami kesulitan dalam menyusun kesimpulan yang logis dan rekomendasi yang relevan. Sedangkan hasil survey setelah pelatihan yaitu peserta menunjukkan peningkatan pemahaman yang signifikan mengenai struktur dan tujuan karya ilmiah. Peserta dapat menyajikan latar belakang penelitian dengan lebih jelas dan koheren setelah pelatihan. Peserta menunjukkan peningkatan kemampuan dalam menyusun desain penelitian dan memilih metode analisis yang tepat. Peserta lebih percaya diri dalam menganalisis data dan menginterpretasikan hasil setelah mengikuti pelatihan.  
Optimasi Strategi Pemasaran E-Commerce Melalui Prediksi Konversi Berbasis Machine Learning Agustina Heryati; Terttiaavini, Terttiaavini; Septa Cahyani; K.Ghazali; Harsi Romli; Iski Zaliman
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7553

Abstract

The research identifies the problem of enhancing e-commerce sales conversion through TikTok amidst intense content competition. The objective of the study is to develop a machine learning-based marketing strategy to analyze user behavior and categorize them into Non-Purchasers and Purchasers.The method employed includes clustering using K-Means, K-Medoids, and Fuzzy C-Means algorithms, with K-Means demonstrating the best performance, achieving the highest Silhouette Coefficient (0.1857) and the lowest Davies-Bouldin Index (1.9991). Following clustering, classification is performed using Naïve Bayes, Decision Tree, and Random Forest algorithms. The Random Forest model yields the best results with an accuracy of 0.9945, showcasing its effectiveness in predicting sales conversions.The conclusion of this study indicates that K-Means and Random Forest are the optimal methods for clustering and classification, respectively, in understanding user behavior on TikTok. These findings can assist e-commerce players in tailoring their marketing strategies, improving sales conversion rates, and enhancing advertising efficiency
Analisis Tren Penjualan dan Prediksi Produk CV. Sentosa Menggunakan Regresi Linier Dona Marcelina; Indah Pratiwi Putri; Evi Yulianti; Agustina Heryati
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7649

Abstract

This study analyzed sales trends and forecasted the sales of CV Sentosa's products, namely Ater 360 New (X1), Bon Bon (X2), Mini Peanut Crackers (X3), and Marie Susu Int (X4), during the period of January 2019 to August 2023. Monthly sales data were processed using exploratory data analysis (EDA) and linear regression to predict sales trends. The linear regression analysis results indicated that X2 and X3 experienced sales growth with a slope of m=0.01, representing an average increase of 0.01 units per month. Conversely, X4 showed a slight decline with m=−0.01, while X1 remained stable with m=−0.00, indicating minimal changes in sales volume. The accuracy evaluation of the predictions based on MAE, MSE, and RMSE showed that X2 had the best performance with MAE 0.14, MSE 0.03, and RMSE 0.19, followed by X1 and X3, which had similar prediction errors. Although X4 initially showed significant growth, its model exhibited higher prediction errors (MAE 0.17, MSE 0.04, RMSE 0.21). This study provides valuable insights for CV Sentosa's business strategies, highlighting X2 and X3 as promising products due to their consistent growth trends and accurate predictions. This research provides a strong foundation for CV Sentosa in formulating more effective marketing strategies and product development in the future
Co-Authors Aldri Frinaldi Dio Resta Permana Dona Marcelina EVI YULIANTI Fauzia Afriyani Harsi Romli Indah Pratiwi Putri Iski Zaliman K.Ghazali Septa Cahyani Terttiaavini, Terttiaavini Try Wulandari