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All Journal Simetris Transformasi: Jurnal Pengabdian Masyarakat INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi RESEARCH : Computer, Information System & Technology Management Generation Journal Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Teknik Informatika (JUTIF) Journal Automation Computer Information System (JACIS) JSITIK: Jurnal Sistem Informasi dan Teknologi Informasi Komputer NERO (Networking Engineering Research Operation) Jurnal Ilmiah Informatika dan Komputer Fokus Abdimas International Conference on Digital Advanced Tourism, Management, and Technology

Adhika Pramita Widyassari

Sekolah Tinggi Teknologi Ronggolawe, Cepu, Indonesia

Author-ID : 2634292

Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Analisis sentimen publik di twitter terhadap pelantikan presiden Prabowo menggunakan algoritma Naïve Bayes Widyassari, Adhika Pramita; Salsabilla, Dea; Amrozi, M. Ali
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i1.28701

Sentiment analysis on social media, especially Twitter, is an effective method to understand public opinion towards political events such as the inauguration of President Prabowo. This study aims to identify the sentiment of the Indonesian people regarding the inauguration of President Prabowo through machine learning-based sentiment analysis using the Multinomial Naïve Bayes algorithm. Data was collected from Twitter with relevant keywords and hashtags, covering the time span before and after the inauguration to capture the dynamics of sentiment changes. The preprocessing process was carried out through text cleaning, removing stop words, tokenization, and stemming to improve model accuracy. The classification results show the distribution of public sentiment, with the majority being neutral (52.63%), followed by positive sentiment (42.98%), and negative (4.39%). The model achieved an accuracy of 75%, showing quite good performance for short text classification. The contribution of this study lies in the application of sentiment analysis to the specific event of the inauguration of the President of Indonesia, with a focus on the critical period before and after the inauguration, which has not been widely studied before. The novelty of this study is the use of real-time Twitter data related to current political events (inauguration of President Prabowo), as well as the emphasis on neutral sentiment which provides a deeper dimension to public understanding. It is hoped that these findings can be the basis for designing more effective public communication strategies on social media.Keywords: naïve bayes, prabowo presidential inauguration, twitter, sentiment analysis

Aplikasi Sistem Pendukung Keputusan Penilaian Kinerja Karyawan untuk Kenaikan Gaji pada PT AAA Widyassari, Adhika Pramita
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 1 No 2 (2017): Vol. 1 No. 2 Agustus 2017
Publisher : Universitas Nusantara PGRI Kediri

PT AAA is a company engaged in the process of instrumentation and electrical design, procurement and construction of instruments. In an effort to improve employee performance PT AAA provide salary increase to employees who have good performance. In employee salary increases are sometimes not in accordance with the results of its performance. Employee salary increases are often hammered flat, or sometimes pay raise only by ingesting it, without the application of strong calculations. That is why there is an application of decision support system employee performance appraisal for a raise. One of the methods used is using Analitycal Hierarchy Process (AHP) method. This method is chosen because it is able to complete the assessment of employee performance based on predetermined criteria, which will be used as a reference in the raise of the salary itself. The results of the application of decision support system employee performance appraisal for salary increase not only determine who the employees are eligible to get a raise but also determine how much percentage of salary increase each employee.

Sistem Pendukung Keputusan Pemilihan Rumah di Kawasan Cepu Menggunakan Analytical Hierarchy Process Widyassari, Adhika Pramita; Yuwono, Teguh
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 3 No 1 (2019): Vol. 3 No. 1 Februari 2019
Publisher : Universitas Nusantara PGRI Kediri

Strategies to buy a house, consumers often experience problems in determining the house to be purchased, because each house has different characteristics. These characteristics will be used as reference considerations in determining which house to buy. In this study, the AHP (Analytical Hierarchy Process) method was used to weight the criteria and test the level of consistency with the pairwise comparison matrix. If the matrix has been consistent, then it can be continued to rank to determine the chosen alternative by using input weighting criteria obtained from the AHP method. To overcome the above problems, it is necessary to build a decision support system by implementing a ranking that can make it easier to determine House Selection using the AHP (Analytical Hierarchy Process) method. Of the 9 sample houses in the Cepu area, the highest was the Housing of Zam Zam Cepu Type 38/78 with a total value of 2,84564719.

Klasterisasi Prioritas Kerusakan Pipa PDAM Blora Cabang Cepu dengan Metode K-Means Anang Triyulianto Nugraha Putra; Adhika Pramita Widyassari
Journal Automation Computer Information System Vol. 6 No. 2 (2026): November (In Progress Issue)
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v6i2.183

Kebocoran pipa distribusi air bersih merupakan permasalahan teknis yang sering terjadi dan berdampak langsung pada efisiensi pelayanan PDAM. Penelitian ini bertujuan untuk mengelompokkan prioritas kerusakan pipa PDAM Blora Cabang Cepu berdasarkan wilayah, ukuran pipa, dan durasi penanganan menggunakan algoritma K-Means. Data yang digunakan sebanyak 228 laporan pengaduan periode 2024-2025. Metode yang diterapkan mengikuti pendekatan Knowledge Discovery in Database (KDD) yang meliputi pengumpulan data, preprocessing, transformasi data, normalisasi Min-Max, penentuan centroid awal, perhitungan jarak Euclidean, serta iterasi hingga konvergensi. Hasil penelitian menghasilkan tiga klaster dengan karakteristik berbeda. Cluster 0 (81 kasus) dan Cluster 2 (124 kasus) dikategorikan sebagai kerusakan ringan dengan ukuran pipa rata-rata 2 inci dan durasi penanganan kurang dari 2 hari (prioritas rendah). Cluster 1 (23 kasus) merupakan kerusakan berat dengan ukuran pipa rata-rata 5,17 inci dan durasi penanganan mencapai 8,57 hari (prioritas tinggi). Ukuran pipa terbukti sebagai indikator yang lebih kuat dalam memprediksi durasi perbaikan dibandingkan wilayah. Kesimpulannya, metode K-Means efektif digunakan untuk menentukan prioritas penanganan kerusakan pipa secara objektif dan dapat dijadikan dasar pengambilan keputusan operasional PDAM Cabang Cepu.

Klasifikasi Terhadap Estimasi Waktu Pengiriman Kurir Lokal Menggunakan Naive Bayes Mohammad Ulin Nuha; Adhika Pramita Widyassari
Journal Automation Computer Information System Vol. 6 No. 2 (2026): November (In Progress Issue)
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v6i2.185

Penelitian ini bertujuan untuk mengimplementasikan metode Naive Bayes dalam klasifikasi estimasi waktu pengiriman kurir lokal pada layanan ShopeeFood di wilayah Blora, Jawa Tengah. Latar belakang penelitian didasari oleh meningkatnya kebutuhan prediksi waktu pengiriman yang akurat dalam industri logistik dan e-commerce guna menjaga kepuasan pelanggan serta efisiensi operasional. Data dikumpulkan dari sistem internal ShopeeFood selama periode 1–28 Februari 2026, dengan total 150 pesanan yang setelah melalui tahap preprocessing menghasilkan 140 data valid. Fitur yang digunakan meliputi restoran, jam pengiriman, jarak tempuh, dan kondisi cuaca. Kategori waktu pengiriman dibagi menjadi tiga kelas: cepat (≤5 menit), sedang (6–10 menit), dan lambat (>10 menit). Data dibagi menjadi data latih (70%) dan data uji (30%) menggunakan hold-out validation dengan stratifikasi. Implementasi Naive Bayes menggunakan jenis Multinomial Naive Bayes dengan smoothing Laplace (α=1). Hasil evaluasi menunjukkan akurasi per kategori mencapai 100% untuk kategori sedang, 92,59% untuk kategori cepat, dan 66,67% untuk kategori lambat. Jarak tempuh terbukti menjadi faktor pembeda utama antar kategori, sementara kondisi cuaca memberikan pengaruh yang relatif kecil. Kesimpulannya, metode Naive Bayes efektif digunakan sebagai alat bantu klasifikasi estimasi waktu pengiriman kurir lokal meskipun masih memiliki keterbatasan pada kelas dengan distribusi data minoritas

Analisis Performa Random Forest dalam Mengklasifikasikan Engagement Rate Konten Video Blora TV Eko Diding Wahyudi; Adhika Pramita Widyassari
Journal Automation Computer Information System Vol. 6 No. 2 (2026): November (In Progress Issue)
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v6i2.186

Penelitian ini bertujuan untuk menganalisis performa algoritma Random Forest dalam mengklasifikasikan engagement rate konten video YouTube Blora TV serta mengidentifikasi faktor-faktor paling berpengaruh terhadap popularitas video. Data dikumpulkan dari kanal YouTube Blora TV sebanyak 125 video dengan delapan atribut utama meliputi durasi, waktu tonton, subscriber, estimasi pendapatan, dan rasio klik-tayang (CTR). Target klasifikasi dibagi menjadi tiga kategori popularitas berdasarkan waktu tonton, yaitu Low (Tidak Populer), Medium (Cukup Populer), dan High (Sangat Populer). Pra-pengolahan data mencakup pembersihan data, normalisasi fitur, dan pembagian data latih (60%) dan data uji (40%) menggunakan stratified split. Model Random Forest dikonfigurasi dengan 100 pohon dan kedalaman maksimal 10. Hasil evaluasi menunjukkan akurasi testing sebesar 98,00%, F1-score tertimbang 97,90%, serta rata-rata validasi silang 5-lipat sebesar 98,40% dengan standar deviasi 1,96%. Analisis feature importance mengungkap bahwa waktu tonton (46,37%) merupakan faktor paling dominan, diikuti oleh subscriber (20,89%) dan CTR (18,84%). Model hanya melakukan satu kesalahan klasifikasi dari 50 data uji, yaitu video kelas High terprediksi sebagai Medium. Kendala utama terletak pada rendahnya recall kelas High (75%) akibat ketidakseimbangan kelas (hanya 7,2% video populer). Secara keseluruhan, Random Forest terbukti efektif, stabil, dan interpretabel dalam mengklasifikasikan engagement rate konten video Blora TV, serta layak dijadikan alat pendukung keputusan strategis berbasis data

Co-Authors Al Hadi, Fahreza Alhadi, Fahreza Amrozi, M Ali Amrozi, M Ali Ali Amrozi, M. Ali An'syah, Mohamad Ardy Anang Triyulianto Nugraha Putra Anggraeni, Arlia Auliya, Ahmad Himam Ayu Saputri, Afi Qur'aini Ayu, Anggi Mutia Cannavaro, Afiq Fadhil Carreon, Jonathan Rante Darmawan, Dwichi Dea Salsabilla Dentaruni Cahya Purnomo Dwi Purnomo Putro Eko Diding Wahyudi Fahrik Huzaeni Fawnia, Nabila Belva Febrianto, Ervian Fitria Rizqi Nurdiana Gajendra, Ibran Hartono, Bayu JUNAIDI, MUKSAN Kevin, Geovanda Masuri, Masuri Mochammad Arip Faizal Mohamad Ardy An’syah Mohammad Ulin Nuha Mudjijanto, Mudjijanto Muntri Yanti Muslimah Katoningati Nagari, Bintang Putra Nur Arrahman, Riski Permadi, Dika Firman Puput Eka Suryani Putro, Dwi Purnomo Rahmasari, Nagita Ratna Dwi Rahayu Rena Cahya Viollita Safrudin, Moch. Yusuf Salma Salsabila, Andini Salsabila, Andini Salma Salsabilla, Dea Sari, Hasna Yustika Setiawan, Cevin Eris Sirojunnafis, Ahmad Sulistiyo, Yogi Sulistyo, Yogi SW, Denni Figo Teguh Yuwono Teguh Yuwono tiodora irnariyanti Umi Rosidah Wahyusari, Retno Wulan, Septia Nawang

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