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All Journal Malcom: Indonesian Journal of Machine Learning and Computer Science Prosiding Seminar Nasional CORISINDO
Ghofar, Khidhir Akbar
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Computer Science & IT

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Pemanfaatan Artificial Intelligence dalam Value Proposition Design untuk Pengembangan Produk Pertanian Lokal di Indonesia Indrayani, Rina; Ghofar, Khidhir Akbar; Pratiwi, Wike
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5612

Abstract

Pertanian lokal Indonesia memiliki peran strategis dalam mendukung ketahanan pangan dan perekonomian nasional. Namun, sektor ini masih menghadapi tantangan berupa rendahnya nilai tambah, keterbatasan akses pasar, dan daya saing global yang relatif lemah. Penelitian ini bertujuan mengintegrasikan Artificial Intelligence (AI) dalam Value Proposition Design (VPD) guna merumuskan strategi pengembangan produk pertanian lokal yang lebih berorientasi pada kebutuhan konsumen. Data dikumpulkan melalui survei konsumen, wawancara petani, laporan pasar, serta analisis sentimen berbasis AI terhadap media sosial. Hasil penelitian memperlihatkan bahwa konsumen Indonesia menunjukkan preferensi kuat terhadap produk sehat, organik, dan ramah lingkungan; 74% responden lebih memilih merek berkelanjutan dan 77% bersedia membayar lebih untuk produk eco-friendly. AI terbukti mampu mengolah big data, mengidentifikasi pola konsumsi, serta menghasilkan prototipe value proposition yang menekankan aspek kesehatan, keberlanjutan, dan keadilan sosial. Temuan ini mengindikasikan bahwa pemanfaatan AI dalam desain proposisi nilai tidak hanya memperkuat relevansi produk pertanian lokal di mata konsumen, tetapi juga membuka peluang peningkatan daya saing dan ekspor. Integrasi AI dengan VPD dapat menjadi pendekatan inovatif bagi petani, UMKM, maupun pelaku industri pertanian dalam menciptakan model bisnis berkelanjutan dan responsif terhadap perubahan tren pasar.
Analisis Pasar Digital Dalam Pengambilan Keputusan Pembelian Produk Skintific Ghofar, Khidhir Akbar; Indriyani, Rina; Septiani, Metta
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5618

Abstract

The development of digital technology has significantly changed consumer behavior in purchasing products, especially skincare products such as Skintific. The purpose of this study is to analyze the factors that influence the decision to purchase Skintific in the digital market. The research method used is a quantitative approach with data collection techniques through online surveys of 150 respondents who are product users. Data analysis was conducted using multiple linear regression and market segmentation. The results of the study show that product quality, price, digital marketing, influencer marketing, and customer reviews have a significant impact on purchasing decisions. In addition, market segmentation shows that female platform users in the 18–25 age group are the dominant segment when purchasing products with Shimizu. The company provides strategic effects to improve product quality and strengthen digital marketing and collaboration with influencers as their main marketing strategy.
ANALISIS DIGITAL PROMOTION DAN STORE ATMOSPHERE TERHADAP KEPUTUSAN PEMBELIAN MIZAN BOOK STORE Ghofar, Khidhir Akbar; Indrayani, Rina
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5620

Abstract

Penelitian ini dilatar belakangi oleh permasalahan yang ada pada Mizan Pustaka Bandung, sebagai perusahaan yang bergerak di bidang ritel khususnya buku sudah melakukan penerapan digital bisnis dan selalu berusaha meningkatkan store atmosphere tetapi volume penjualan setiap bulannya mengalami penurunan. Tujuan dari penelitian ini adalah untuk mengetahui gambaran serta pengaruh Digital Promotion dan store atmosphere terhadap keputusan pembelian secara parsial dan simultan pada mizan pustaka bandung. Pendekatan yang digunakan untuk memecahkan masalah dari penelitian ini yaitu menggunakan metode kuantitatif deskriptif dan verifikatif. Jumlah responden penelitian ini ada 100 responden, hasil tanggapan responden dalam penelitian ini digambarkan baik serta berpengaruh positif secara parsial dan simultan, hasil dari penelitian ini juga menjelaskan bahwa kontribusi atau sumbangan pengaruh Digital Promotion dan store atmosphere terhadap keputusan pembelian yaitu sangat baik. Kesimpulan dari penelitian ini bahwa, terdapat pengaruh positif secara parsial maupun simultan dari variabel Digital Promotion dan Store Atmosphere Terhadap Keputusan Pembelian.
Tourism Marketing Intelligence: Machine Learning for Demand Forecasting Using Google Trends in Indonesia Ghofar, Khidhir Akbar; Hidayat, Muhammad Alif; Adib, Adib
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2628

Abstract

Tourism demand forecasting remains challenging due to the dynamic nature of consumer behavior and the lag in traditional economic indicators. This study investigates the predictive power of online search behavior captured through Google Trends in forecasting international tourist arrivals to Indonesia, integrating digital footprints with macroeconomic indicators using machine learning approaches. Monthly data spanning January 2011 to December 2024 (N=168) were collected from multiple sources: tourist arrivals from Statistics Indonesia, Google Trends composite index from 11 destination-related keywords, exchange rates, and consumer price index. Three machine learning algorithms Random Forest, XGBoost, and LSTM were compared using temporal train-test split (80:20). Random Forest demonstrated superior performance with Test MAPE of 14.12% and R² of 0.811, outperforming XGBoost (MAPE 21.41%) and LSTM (MAPE 24.56%). Feature importance analysis revealed that Google Trends emerged as the second most important predictor (12.22% contribution), surpassing all economic indicators combined (7.20%), validating its role as a leading indicator of tourism demand. This study contributes methodologically by providing empirical evidence for the value of integrating digital behavioral signals with traditional predictors in emerging destination contexts, and practically by demonstrating deployment-ready forecasting accuracy for tourism stakeholders in Indonesia.
Co-Authors Adib Adib, Adib Hidayat, Muhammad Alif Indriyani, Rina Pratiwi, Wike Rina Indrayani Septiani, Metta