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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Informatika dan Teknik Elektro Terapan JISKa (Jurnal Informatika Sunan Kalijaga) Academia Open JPM: JURNAL PENGABDIAN MASYARAKAT Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi NUSANTARA: Jurnal Pengabdian Kepada Masyarakat SENTRI: Jurnal Riset Ilmiah Kybernology : Journal of Government Studies Cahaya Pengabdian
Rauhulloh Ayatulloh Khomeini Noor Bintang
Universitas Muhammadiyah Karanganyar
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Journey to Smart City: The Case of Smart City Development in Karanganyar Regency Huda, Muhammad Nurul; Samsuri, Muhammad; Bintang, Rauhulloh Ayatulloh Khomeini Noor
Kybernology : Journal of Government Studies Vol. 3 No. 2 (2023): Oktober 2023
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/kjgs.v3i2.12780

Abstract

A smart city is a modern and advanced city that is integrated with digital systems that support convenience and comfort for its residents. Initially, the smart city concept was aimed at developed countries that have adequate infrastructure, but in Indonesia, district or city governments can improvise in its implementation according to the needs and capabilities of local governments. The aim of this research is to analyze the obstacles and strategies for implementing smart cities that are adapted based on the conditions of the Karanganyar Regency area. The research method uses descriptive-qualitative research, which is linked to empirical reality with applicable theory. Data is obtained from journal references, regional regulations, the Karanganyar Regency smart city master plan book, etc. The results show that the journey towards a smart city in Karanganyar Regency has been documented in the 2018–2023 RPJMD, but its implementation is faced with infrastructure (technology), structural (HR and budget), and superstructure (institution and policy) problems.
CLASSIFICATION OF SMS SPAM WITH N-GRAM AND PEARSON CORRELATION BASED USING MACHINE LEARNING TECHNIQUES Romadloni, Nova Tri; Septiyanti, Nisa Dwi; Pratomo, Cucut Hariz; Kurniawan, Wakhid; Bintang, Rauhulloh Ayatulloh Khomeini Noor
SENTRI: Jurnal Riset Ilmiah Vol. 3 No. 2 (2024): SENTRI : Jurnal Riset Ilmiah, February 2024
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v3i2.2252

Abstract

The Short Message Service (SMS) has garnered widespread popularity due to its simplicity, reliability, and ubiquitous accessibility.This study aims to enhance the efficacy of SMS classification by refining the classification process itself. Specifically, it strives to streamline the process by diminishing feature dimensions and eliminating inconsequential attributes. The textual data undergoes preprocessing, which involves employing the N-Gram technique for feature representation, followed by meticulous feature selection utilizing Pearson Correlation. The study employs 5 of classification algorithms. Notably, the findings underscore that the optimal outcomes emerge from the fusion of the N-Gram methodology with feature selection through Pearson Correlation. Among these, the Support Vector Machine methodology stands out, exhibiting a remarkable 91.41% enhancement in accuracy without feature selection, a further improvement to 91.96% through N-Gram utilization, and a final performance of 70.80% following the inclusion of weighted correlation. However, it is imperative to acknowledge the limitations inherent in the model's generalizability, primarily stemming from the utilization of a relatively modest dataset. Despite the efficacy of Pearson correlation and N-gram-based feature selection in curbing data dimensionality and enhancing processing efficiency, certain pertinent features may have been overlooked, or the chosen attributes might not be optimally suited for specific classifications.
ANALISIS DAMPAK CACHE PROGRESSIVE WEB APPS TERHADAP KONSUMSI BATERAI ANDROID Kurniawan, Wakhid; Romadloni, Nova Tri; Noor Bintang, Rauhulloh Ayatulloh Khomeini
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 2 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i2.6221

Abstract

Penggunaan aplikasi web berkembang pesat, terutama di Android yang menguasai 46,18% pangsa pasar global. Pengguna menginginkan akses cepat, namun sering menghadapi koneksi lambat dan pemuatan ulang aset tanpa cache, yang dapat meningkatkan konsumsi baterai. Salah satu faktor yang diduga berpengaruh adalah penggunaan cache dalam aplikasi. Progressive Web Apps (PWA) menjadi relevan karena memanfaatkan service worker untuk menyimpan cache. PWA menawarkan keunggulan seperti akses tanpa koneksi, pemrosesan latar belakang, dan notifikasi push, memberikan pengalaman serupa aplikasi native. Penelitian ini menganalisis dampak cache PWA terhadap konsumsi baterai Android. Metode yang digunakan bersifat kuantitatif dengan eksperimen empiris. Sebanyak 33 situs PWA dipilih menggunakan Google Lighthouse. Data ukuran cache dikumpulkan, dan laporan bug dihasilkan selama 3 menit untuk mengukur konsumsi daya. Analisis dilakukan menggunakan uji Paired Sample T-Test dengan SPSS, membandingkan konsumsi baterai saat cache kosong dan terisi. Penelitian ini bertujuan memberikan wawasan mengenai pengaruh cache terhadap konsumsi daya, sehingga strategi dapat dikembangkan untuk meningkatkan efisiensi energi dan pengalaman pengguna.
PERBANDINGAN KINERJA ALGORITMA KLASIFIKASI PADA REVIEW PENGGUNA APLIKASI NETFLIX KHOMEINI NOOR BINTANG, RAUHULLOH AYATULLOH; Romadloni, Nova Tri
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 2 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i2.6303

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Netflix yang diperoleh dari Google Play Store menggunakan metode web scraping dengan Python di Google Colab. Data ulasan diproses melalui tahap pembersihan teks, tokenisasi, penghapusan stopword, dan stemming, serta direpresentasikan menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF). Lima algoritma klasifikasi, yaitu Logistic Regression, Naive Bayes, Decision Tree, Random Forest, dan Support Vector Machine (SVM), dibandingkan untuk menentukan algoritma terbaik dalam klasifikasi sentimen positif, negatif, dan netral. Evaluasi dilakukan berdasarkan akurasi dengan pembagian data latih dan data uji sebesar 90:10. Hasil pengujian menunjukkan bahwa Logistic Regression dan Random Forest memiliki akurasi tertinggi sebesar 76%, diikuti oleh SVM sebesar 74%, Decision Tree sebesar 73%, dan Naive Bayes dengan akurasi terendah sebesar 71%. Temuan ini memberikan kontribusi bagi penelitian di bidang analisis sentimen serta dapat menjadi referensi bagi pengembang aplikasi dalam meningkatkan pengalaman pengguna berbasis data.
Co-Authors Anton Yudhana Cucut Hariz Pratomo Cucut Hariz Pratomo Dewantara, Rizki Gatra, Rahmadhan Kurniawan, Wakhid Muhammad Ardiansyah Muhammad Nurul Huda Muhammad Nurul Huda Muhammad Samsuri Nisa Dwi Septiyanti Nova Tri Romadloni Pamastutiningtyas , Terrensia Sekar Penatari, Resi Intan Pratomo, Cucut Hariz Resi Intan Penatari Rusydi Umar Samsuri, Muhammad Wakhid Kurniawan Wakhid Kurniawan