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All Journal Prosiding Seminar Nasional Teknoka IJID (International Journal on Informatics for Development) TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Irfan Ricky Afandi
Universitas Muhammadiyah Prof. Dr. HAMKA
Computer Science & IT Engineering

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Sistem Informasi Penjualan Online Hasil Tani Desa Blukbuk Berbasis Aplikasi Website. Irfan Ricky Afandi; Yusuf Handika; Isa Faqihuddin Hanif; Dede Ismail
Prosiding Seminar Nasional Teknoka Vol 5 (2020): Prosiding Seminar Nasional Teknoka ke - 5
Publisher : Fakultas Teknik, Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Blukbuk Village is a village where most of the people's livelihoods are in the agricultural sector. So far, the process of selling agricultural products in Blukbuk Village is still carried out conventionally. However, farmers in Blukbuk Village often experience difficulties in selling agricultural products to consumers. The purpose of this research is to assist farmers in Blukbuk Village in selling agricultural products to consumers through an online sales information system based on a website application using the PHP programming language and MySQL database. With the design of an online sales information system based on a website application, it is hoped that it can introduce agricultural products in Blukbuk Village to the wider community and can improve the economy in Blukbuk Village
Sentiment Analysis of TIMNAS Indonesia's Participation in the Asian Cup U23 2024 on X Using Naive Bayes and SVM Sewin Fathurrohman; Irfan Ricky Afandi; Firman Noor Hasan
IJID (International Journal on Informatics for Development) Vol. 13 No. 1 (2024): IJID June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4504

Abstract

This study aims to analyze the sentiment of the Indonesian public regarding the participation of the Indonesian National Team in the 2024 U-23 Asian Cup through the social media platform X. Sentiment analysis is crucial for understanding public perception and its impact on support for the national team. The research methodology involves collecting user comments on X related to the team's performance during the tournament, followed by data cleaning. The dataset is manually labeled, with 80% used as training data for algorithmic model training and the remaining 20% as test data, classified using Naive Bayes and Support Vector Machine algorithms. The analysis results indicate that the SVM algorithm achieves a higher % accuracy rate of 95% compared to Naive Bayes, which achieves 87%. The majority of the 3367 opinions analyzed express positive or satisfactory sentiments towards the national team's participation. However, there are fewer negative sentiments, highlighting areas requiring team management's attention. This study provides valuable insights into public perception of the Indonesian National Team. Furthermore, these findings can inform policymakers and team managers' decision-making to enhance the team's quality and performance in the future.
Sentiment Analysis on Shopee Xpress Delivery Time Reviews Using Support Vector Machine and Logistic Regression Sewin Fathurrohman; Irfan Ricky Afandi; Irma Wahyuningtyas; Azis Styo Nugroho; Firman Noor Hasan
IJID (International Journal on Informatics for Development) Vol. 14 No. 2 (2025): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.5073

Abstract

This study examines user sentiment towards Shopee Xpress delivery times using machine learning techniques. We collected 497 reviews from platforms like X and the Google Play Store, leveraging the valuable feedback despite its unstructured and informal nature. After labelling 398 reviews for model training and reserving 99 for sentiment prediction, we implemented two classification algorithms: Support Vector Machine (SVM) and Logistic Regression. These models categorised sentiments into negative, neutral, and positive classes. Despite class imbalance in the training data, SVM outperformed Logistic Regression with an accuracy of 93%, demonstrating a more balanced performance across sentiment categories compared to Logistic Regression's 90% accuracy. Both models showed consistent sentiment prediction on new data. Our findings highlight the potential of sentiment analysis as a valuable tool for Shopee Xpress to understand customer perceptions and improve delivery experiences. By providing actionable insights, this study can inform logistics improvements and enhance customer satisfaction. Future research could benefit from collaborating with Shopee to access internal data and integrating additional data sources for more comprehensive insights, ultimately driving business growth and customer loyalty. This study contributes to the growing body of research on sentiment analysis in logistics and e-commerce.
Aplikasi SIPEDRO 1.0 untuk pemantauan hidroponik dengan platform blynk terintegrasi ESP32 Irfan Ricky Afandi; Dimas Febriawan; Annisa Shifah Fauziah Faturohman; Fasya Nazihah; Muhammad Ardhi Andreansyah; Bima Alfian
TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika Vol 10 No 1 (2023): TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Publisher : LPPMPK- Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/tekno.v10i1.334

Abstract

Proses pemantauan merupakan hal terpenting dalam budidaya hidroponik. Salah satu tempat yang membudidayakan sayuran hidroponik di daerah Bogor yaitu Kios Hidroponik 21. Kendala yang dihadapi mitra dalam proses pemantauan hidroponik yaitu masih dilakukan secara konvensional dimana mitra harus mengukur nilai nutrisi, pH, serta suhu pada wadah setiap harinya dan menambahkan larutan apabila nilai nutrisi dan pH tidak sesuai untuk tumbuhan. Hal tersebut membuat proses pemantauan hidroponik menjadi kurang efektif dari segi waktu, tenaga serta biaya. Penelitian ini bertujuan untuk membantu mengatasi permasalahan mitra dalam proses pemantauan hidroponik dengan menerapkan teknologi Internet of Things. Sehingga peneliti memiliki inovasi teknologi yaitu membuat aplikasi sistem pemantauan hidroponik (SIPEDRO 1.0). Kelebihan dari aplikasi tersebut yaitu dapat melakukan pemantauan nutrisi, pH serta suhu secara realtime dan dapat memberikan nutrisi serta pH yang ideal secara otomatis dengan menggunakan aplikasi android yang sudah dibuat dengan platform Bylink. Metode pengembangan sistem yang pakai yaitu waterfall serta menggunakan black box testing. Hasil dari uji coba sistem SIPEDRO 1.0 memperlihatkan aplikasi yang sudah dibuat telah berjalan dengan baik serta dapat menampilkan hasil pembacaan sensor nutrisi, sensor pH, sensor suhu, mengatur nilai nutrisi yang ideal, mengatur nilai pH yang ideal serta menampilkan grafik dari hasil pembacaan sensor suhu melalui aplikasi android SIPEDRO 1.0.
Co-Authors Annisa Shifah Fauziah Faturohman Annisa Shifah Fauziah Faturohman Azis Styo Nugroho Bima Alfian Dede Ismail Dimas Febriawan Fasya Nazihah Firman Noor Hasan Irma Wahyuningtyas Isa Faqihuddin Hanif Muhammad Ardhi Andreansyah Sewin Fathurrohman Yusuf Handika