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All Journal REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer INOVTEK Polbeng - Seri Informatika
Rusdi Hamidan
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Computer Science & IT Decision Sciences, Operations Research & Management Education

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Sentiment Analysis on Social Media Instagram of Depression Issues Using Naïve Bayes Method Voni Anggraeni Suwito Putri; Vitianingsih, Anik Vega; Rusdi Hamidan; Anastasia Lidya Maukar; Niken Titi Pratitis
INOVTEK Polbeng - Seri Informatika Vol. 9 No. 2 (2024): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/spchsk42

Abstract

The rapid expansion of the digital era has turned social media, especially Instagram, into a crucial source for examining public sentiment on mental health issues such as depression. Depression, a condition that adversely affects thoughts, behaviours, emotions, and overall mental well-being, is often less apparent than physical health problems, leading to delays in treatment. Low public awareness and societal stigma further aggravate these delays, making sufferers hesitant to seek professional help and more inclined to share their experiences online. This study aims to analyze public sentiment on Instagram concerning depression through the Naïve Bayes (NB) method. It involves developing an application that visualizes analysis reports via bar and pie charts, categorizing public comments on depression as neutral, positive, or negative. Data is sourced from Instagram using keywords related to depression and mental health, with lexicon-based methods for labelling and NB for sentiment classification. The findings show the effectiveness of this method, with the accuracy rate reaching 79%. The dataset consists of 1300 comments collected through web crawling. This evaluation displays the performance results of NB achieving an accuracy of 82.55%. The study aims to offer insights into public opinions on depression, provide datasets for future sentiment analysis research, and assess the NB method's effectiveness in managing complex sentiments on social media, ultimately aiming to improve public understanding and strategies for mental health intervention.
Analisis Fungsionalitas dan Usability Aplikasi Landing Page Dinamis sebagai Instrumen Manajemen Leads PMB (Studi Kasus: Universitas Dr. Soetomo) Achmad Choiron; Rusdi Hamidan
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 10 No. 1 (2026): Volume 10 Nomor 1 Januari 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v10i1.15831

Abstract

Persaingan ketat dalam Penerimaan Mahasiswa Baru (PMB) menuntut institusi pendidikan untuk beralih dari strategi pemasaran statis menuju personalisasi digital. Universitas Dr. Soetomo (Unitomo) menghadapi tantangan dalam menyajikan informasi yang relevan dan mengelola data calon mahasiswa (leads) secara terstruktur. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi aplikasi landing page dinamis yang terintegrasi dengan dashboard manajemen leads. Metode penelitian menggunakan pendekatan Research and Development (R&D) dengan tahapan verifikasi melalui pengujian Blackbox serta Whitebox, dan tahapan validasi melalui pengujian usability menggunakan instrumen kuesioner kustom dan System Usability Scale (SUS). Hasil pengujian Blackbox dan Whitebox menunjukkan bahwa seluruh fungsionalitas sistem berjalan 100% sukses dengan struktur logika yang efisien. Pada sisi pengguna (front-end), aplikasi memperoleh skor rata-rata kepuasan 4,15 dari 5,00, yang mengindikasikan kategori "Sangat Baik". Namun, pada sisi administrator (backend), pengujian SUS menghasilkan skor rata-rata 61,25, yang menempatkan sistem pada kategori "Marginal High" (Grade D). Temuan menunjukkan bahwa meskipun sistem sangat efektif dalam menarik leads, dashboard admin memerlukan perbaikan pada fitur ekspor data dan penyederhanaan antarmuka untuk meningkatkan pengalaman pengguna internal.
Co-Authors Achmad Choiron Anastasia Lidya Maukar Anik Vega Vitianingsih Niken Titi Pratitis Voni Anggraeni Suwito Putri