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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan IJID (International Journal on Informatics for Development) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) PROFICIO: Jurnal Pengabdian Masyarakat Husnayain Business Review SENTRI: Jurnal Riset Ilmiah Eduvest - Journal of Universal Studies Cahaya Pengabdian
Kurniawan, Wakhid
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Aerospace Engineering Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Law, Crime, Criminology & Criminal Justice Neuroscience Social Sciences Other

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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.
A Hybrid Approach of Pearson Correlation and PCA in Feature Selection for Opinion Mining Tri Romadloni, Nova; Kurniawan, Wakhid; Ariyadi, Muhammad Yusuf; Efendi, Burhan
IJID (International Journal on Informatics for Development) 2025
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

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

Abstract

This study proposes a hybrid feature selection approach that combines Pearson Correlation and Principal Component Analysis (PCA) to improve classification performance in opinion mining tasks. The rapid growth of e-commerce on social media platforms, such as TikTok, has generated a significant volume of user-generated reviews, which are valuable sources of consumer sentiment. However, the high dimensionality of textual data poses challenges in achieving accurate sentiment classification. To address this issue, the proposed method first applies Pearson Correlation to remove irrelevant features with weak correlation to sentiment labels, followed by PCA to reduce dimensionality. The dataset consists of user reviews from the TikTok Seller platform. Experiments using SVM, Naive Bayes, and Random Forest show that the hybrid approach achieves the highest accuracy of 86.2% (SVM and RF), improving over PCA-only by +0.9% and recovering 13.8% accuracy loss for Naive Bayes (from 72.0% to 83.1%). The results demonstrate that integrating correlation- and projection-based methods yields a more compact and effective feature set. This approach is especially suited for opinion mining in noisy, high-dimensional e-commerce data.
Uncovering Insights in Spotify User Reviews with Optimized Support Vector Machine (SVM) Tri Romadloni, Nova; Kurniawan, Wakhid
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): 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.2025.4903

Abstract

The rapid growth of user-generated reviews on platforms like Spotify necessitates efficient analytical techniques to extract valuable insights.  This study employs a Support Vector Machine algorithm, optimized using Forward Selection, Backwards Elimination, Optimized Selection, Bagging, and AdaBoost, to effectively classify user reviews. A dataset of approximately 10,000 Spotify reviews was compiled from diverse online sources, ensuring a representative sample. The analysis reveals sentiment patterns across positive, negative, and neutral categories, with positive reviews dominates the landscape. These patterns help highlight Spotify’s strengths while identifying areas for improvement. However, the SVM algorithm faces challenges in classifying minority classes, particularly negative sentiments, due to class imbalance. To address this, advanced optimization techniques are utilized to enhance classification precision and recall. Preprocessing steps, including data cleansing, tokenization, stemming, and stopword removal, refine the dataset, while TF-IDF converts text into numerical features for effective feature selection. The results show that the Optimized Selection method achieves the highest accuracy of 84.5%, outperforming other approaches. This research contributes significantly to developing balanced sentiment analysis models. Future studies may explore deep learning techniques to further improve classification accuracy and mitigate current limitations in data representation.
A Hybrid Approach of Pearson Correlation and PCA in Feature Selection for Opinion Mining Tri Romadloni, Nova; Kurniawan, Wakhid; Ariyadi, Muhammad Yusuf; Efendi, Burhan
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.2025.5195

Abstract

This study proposes a hybrid feature selection approach that combines Pearson Correlation and Principal Component Analysis (PCA) to improve classification performance in opinion mining tasks. The rapid growth of e-commerce on social media platforms, such as TikTok, has generated a significant volume of user-generated reviews, which are valuable sources of consumer sentiment. However, the high dimensionality of textual data poses challenges in achieving accurate sentiment classification. To address this issue, the proposed method first applies Pearson Correlation to remove irrelevant features with weak correlation to sentiment labels, followed by PCA to reduce dimensionality. The dataset consists of user reviews from the TikTok Seller platform. Experiments using SVM, Naive Bayes, and Random Forest show that the hybrid approach achieves the highest accuracy of 86.2% (SVM and RF), improving over PCA-only by +0.9% and recovering 13.8% accuracy loss for Naive Bayes (from 72.0% to 83.1%). The results demonstrate that integrating correlation- and projection-based methods yields a more compact and effective feature set. This approach is especially suited for opinion mining in noisy, high-dimensional e-commerce data.
SENAM HIPERTENSI UNTUK MENGURANGI RISIKO STROKE PADA KADER POSYANDU DUKUH TRENGGULI JENAWI KARANGANYAR Prasetyo, Afif Bayu Eko; Kurniawan, Wakhid; Muhammad Demas; Intan Ika Nabila; Nur Afifah; tutut
Jurnal Pengabdian Masyarakat FKIP UTP Vol 7 No 1 (2026): PROFICIO : Jurnal Abdimas FKIP UTP
Publisher : FKIP UNIVERSITAS TUNAS PEMBANGUNAN SURAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36728/jpf.v7i1.5668

Abstract

Hipertensi merupakan salah satu faktor risiko utama terjadinya stroke. Kader kesehatan masyarakat, khususnya di Posyandu, memiliki peran strategis dalam pencegahan komplikasi hipertensi. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan pengetahuan dan keterampilan kader Posyandu tentang senam hipertensi sebagai upaya pencegahan stroke di Dukuh Trengguli, Kecamatan Jenawi, Kabupaten Karanganyar. Metode yang digunakan adalah penyuluhan kesehatan, demonstrasi, dan praktik senam hipertensi. Peserta terdiri dari 25 kader Posyandu dengan kriteria berusia 25-60 tahun, aktif sebagai kader minimal 1 tahun, sehat jasmani, dan bersedia mengikuti kegiatan lengkap. Kegiatan dilakukan melalui penyuluhan kesehatan (60 menit), demonstrasi dan praktik (90 menit), serta diskusi (30 menit). Evaluasi dilakukan melalui pre-test dan post-test serta observasi kemampuan praktik senam. Hasil menunjukkan peningkatan pengetahuan peserta tentang hipertensi dari 65% menjadi 85% (peningkatan 30,7%) dan pengetahuan tentang senam hipertensi dari 45% menjadi 80% (peningkatan 77,8%). Seluruh peserta mampu melakukan gerakan senam hipertensi dengan benar setelah pelatihan, dengan tingkat keberhasilan 96% untuk gerakan pemanasan, 88% untuk gerakan inti, dan 92% untuk gerakan pendinginan. Kesimpulan adalah program senam hipertensi efektif meningkatkan pengetahuan dan keterampilan kader Posyandu dalam pencegahan komplikasi hipertensi serta dapat menjadi model untuk kegiatan pengabdian serupa. Kata Kunci: senam hipertensi; stroke; kader posyandu; pencegahan; pengabdian masyarakat
Co-Authors Agung Fatwanto, Agung Apriliyanto, Erwin Ardiansyah, Hafizd Chuaynugul , Sompong Dimas Fajar Nugroho Efendi, Burhan Endi Rekarti, Endi Herawati, Shabrina Intan Ika Nabila Irawati, Diwi Acita Khoirul Umam Martino Wibowo Muhammad Ardiansyah Muhammad Demas Muhammad Yusuf Ariyadi Nafi'ah, Rahmawati Nisa Dwi Septiyanti Nova Tri Romadloni Nur Afifah Oktavianita, Annisa Dwi Prasetyo, Afif Bayu Eko Pratomo, Cucut Hariz Puji Astuti Putra, Romi Iriandi Rauhulloh Ayatulloh Khomeini Noor Bintang Setiawan, Johan Tahe, Mr. Fitree Tutut