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APLIKASI SMART VILLAGE DALAM PENERAPAN GOVERMENT TO CITIZEN BERBASIS MOBILE PADA KELURAHAN CANDIMAS NATAR Erwanto, Erwanto; Megawaty, Dyah Ayu; Parjito, Parjito
TELEFORTECH : Journal of Telematics and Information Technology Vol 2, No 2 (2021): TELEFORTECH VOL 2 No. 2 (JANUARI 2022)
Publisher : Fakultas Teknik dan Ilmu Komputer, Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/tft.v2i2.3704

Penerapan smart village saat ini masih belum banyak diterapkan seperti kelurahanCandimas Kecamatan Natar yang berlokasi di Kabupaten Lampung Selatan dan memiliki jumlah penduduk 10470 Jiwa dengan 226 Kepala Keluarga. Berdasarkan jumlah penduduk tersebut tentunya pihak desa perlu meningkatan layanan kepada masyarakat sebagai bentuk inovasi berupa desa pintar dengan memanfatkan teknologi informasi.Berdasarkan hasil wawancara yang dilakukan kepada pihak kelurahan diperoleh permasalahan seperti proses pengolahan data yang dilakukan secara keseluruhan masih manual yaitu dengan pencatatan pada buku maupun media cetak melalui media office, hal tersebut berdampak pada proses pengolahan data yang lambat, kerusakan data akibat data arsip berupa media kertas hingga kehilangan dan manipulasi data. Permasalahan berikutnya yaitu penyampaian informasi kepada masyarakat berupa kegiatan maupun pengumuman masih dilakukan menggunakan papan pengumuman ataupun menggunakan pamflet, sehingga dampak yang timbul yaitu tingginya biaya operasional dan cakupan informasi yang terbatas.Metode yang digunakan yaitu extreme programming dengan pembentukan sistem berorientasi objek serta media penyimpanan menggunakan Mysql. Tujuan penelitian yang dilakukan untuk menghasilkan media informasi bagi masyarakat kepada keluarahan untuk memperoleh layanan. Hasil penelitian berupa aplikasi mobile yang diakses oleh masyarakat untuk mempermudah melakukan permohonan surat, pengaduan, kritik dan info pajak serta informasi. Kata Kunci: Smart Village, Government To Citizen, Kelurahan Candimas, Natar

Analisis Sentimen Opini Publik Program Makan Siang Gratis dengan Random Forest Pada Media Azhari, Muhamad; Parjito, Parjito
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6423

The "Free Lunch Program," introduced as part of the 2024 Indonesian election campaign, became a hot topic on social media, especially on the platform X. This program aims to improve children's health and nutrition while reducing stunting rates by providing free lunches and milk to children and pregnant women. A study was conducted to analyze public sentiment regarding the program using the Random Forest algorithm. The data consisted of 9,347 tweets collected through web crawling. The analysis revealed that the majority of sentiments were negative (8,021 entries), while positive sentiments accounted for only 430 entries. The preprocessing steps included data cleaning, case folding, tokenization, stopword removal, and stemming. The imbalance between positive and negative sentiment data was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), resulting in a more balanced dataset. After applying SMOTE, the model achieved 100% accuracy, with significant improvements in precision, recall, and F1-Score. The analysis showed that positive sentiments focused on the program's health and educational benefits, while negative sentiments highlighted criticism of implementation and budget allocation. This study demonstrates the value of sentiment analysis in evaluating social programs and understanding public perceptions.

Analisis Sentimen Publik terhadap Virus HMPV Berdasarkan Media Sosial X dengan Algoritma Logistic Regression Wijaya, Feri Aldi; Parjito, Parjito
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.7053

Human Metapneumovirus (HMPV) is a virus that affects the respiratory tract, causing flu-like symptoms such as cough, fever, and nasal congestion. This virus was first discovered in 2001 and generally causes mild infections. However, certain groups, such as children, the elderly, and individuals with weakened immune systems, are at higher risk of developing severe conditions like bronchitis or pneumonia. Based on this issue, a sentiment analysis of public responses to Human Metapneumovirus (HMPV) cases was conducted using data collected from the X platform, consisting of 10,199 tweets. The data was gathered between December 1, 2024, and January 30, 2025, using Tweet Harvest in Google Colab with the Twitter API. This study applied the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalance, with an 80% to 20% split between training and testing data. The results showed that before applying SMOTE, the logistic regression algorithm had an accuracy of 83%, with precision for positive sentiment at 90%, neutral at 80%, negative at 85%, while recall for positive sentiment was 89%, neutral 89%, negative 92%. After applying SMOTE, accuracy increased to 90%, with the most significant improvement observed in positive sentiment. The precision for positive sentiment reached 90%, neutral 87%, and negative 95%, while recall for positive sentiment was 96%, neutral 90%, negative 84%. This research provides insights into the use of logistic regression algorithms in sentiment analysis related to HMPV and serves as a reference for governments and health organizations in designing more effective communication strategies and interventions.

ANALISIS SENTIMEN TWITTER TERHADAP KONFLIK DI PAPUA MENGGUNAKAN PERBANDINGAN NAIVE BAYES DAN SVM Saputra, M Rafli; Parjito, Parjito
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6180

Konflik di Papua merupakan isu yang kompleks dan telah berlangsung selama beberapa dekade, melibatkan berbagai faktor seperti politik, ekonomi, sosial, dan budaya. Ketegangan antara pemerintah Indonesia dan kelompok separatis Papua sering kali memicu konflik bersenjata, pelanggaran hak asasi manusia, dan ketidakstabilan regional. Konflik ini juga menarik perhatian berbagai pihak, termasuk masyarakat internasional dan pengguna media sosial, khususnya di platform Twitter.Data yang dikumpulkan dari Twitter menggunakan kata kunci terkait konflik Papua. Proses analisis meliputi tahap pengumpulan data, pra-pemrosesan teks, dan pelabelan sentimen (positif, negatif, netral). Penelitian ini menggunakan 5723 data yang diperoleh melalui teknik web scraping terkait topik tersebut. Tujuan penelitian ini adalah membandingkan performa dua algoritma klasifikasi yang populer dalam analisis sentimen, yaitu Naïve Bayes dan Support Vector Machine (SVM). Sebelum perbandingan dilakukan, optimasi SMOTE (Synthetic Minority Over-sampling Technique) digunakan untuk menyeimbangkan jumlah data minoritas dan mayoritas, sehingga kedua algoritma dapat belajar secara lebih efektif dari setiap kelas sentimen. Hasil perbandingan menunjukkan bahwa algoritma Naïve Bayes memiliki akurasi sebesar 95%, sedangkan SVM mencapai akurasi 99%, dengan presisi 99%, recall 98%, dan F1-Score 99%. Evaluasi performa dilakukan dengan menganalisis confusion matrix dari setiap algoritma. Kesimpulannya, SVM dapat menjadi pilihan yang lebih baik untuk analisis sentimen mengenai konflik Papua. Penelitian ini memberikan kontribusi penting dalam memahami opini masyarakat terkait konflik di Papua.

PERSEPSI PUBLIK TERHADAP KEPEMIMPINAN FIRLI BAHURI DI KPK: PENDEKATAN SENTIMEN TWITTER DENGAN NAÏVE BAYES DAN SVM Tono, Jimmy Julian; Parjito, Parjito
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6181

Komisi Pemberantasan Korupsi (KPK) merupakan lembaga independen Indonesia yang bertujuan melakukan pemberantasan korupsi. Di bawah kepemimpinan Firli Bahuri (2019-2023), KPK menangani kasus besar di sektor infrastruktur dan keuangan, serta meluncurkan program pencegahan korupsi. Namun, kepemimpinannya diwarnai kontroversi, termasuk dugaan pelanggaran etik dan tuduhan korupsi, seperti kasus pemerasan terhadap Menteri Pertanian Syahrul Yasin Limpo. Untuk menganalisis respon masyarakat dengan menggunakan data Twitter, peneliti melakukan perbandingan dengan menggunakan metode pengklasifikasian Naïve Bayes dan Support Vector Machine (SVM). Sebelum dilakukan perbandingan antara kedua model, penelitian ini terlebih dahulu melakukan proses optimasi SMOTE untuk mengatasi ketidakseimbangan data agar data minority dan data majority dapat memiliki jumlah data yang sama. Hasil dari perbandingan kedua model mendapatkan nilai algoritma Naïve bayes dengan akurasi 97% dan algoritma support vector machine dengan akurasi 100%, presisi 100%, recall 100%, dan FI-score 100%. Jika dilihat pada perbandingan pada kedua model algoritma, dapat disimpulkan bahwa algoritma SVM mampu melakukan proses analisis sentimen dengan sangat akurat pada setiap tahapannya dibandingkan dengan naïve bayes. Oleh karena itu, dalam penelitian ini terkait opini masyarakat di media sosial twitter terhadap kepemimpinan firli bahuri di kpk, dapat disimpulkan bahwa algoritma SVM menjadi pilihan yang lebih baik dibandingkan algoritma Naïve bayes.

Rancang Bangun Sistem Pemantau Visual Sikap Robot humanoid Studi Kasus Robot Sepak Bola Humanoid Krakatau Football Club Pratama, Edvan Agus; Putra, M. Pajar Kharisma; Samsugi, Selamet; Parjito, Parjito
Jurnal Tekno Kompak Vol 18, No 2 (2024): AGUSTUS
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v18i2.4501

Robot adalah sebuah alat mekanik yang dapat melakukan tugas fisik, baik menggunakanpengawasan dan kontrol manusia, ataupun menggunakan program yang telah didefinisikan terlebih dulu.Salah satu sistem yang terdapat pada robot sepak bola humanoid adalah sistem pemantau visual yang dimana hal itu dapat membantu dalam pengembangan kontrol robot humanoid terutama robot humanoid sepak bola. Sistem pemantau visual adalah sebuah siklus kegiatan yang meliputi proses pengumpulan, peninjauan ulang, pelaporan dan tindakan atas informasi suatu proses yang sedang diimplementasikan. Pengendalian yang dilakukan melalui komputer memungkinkan pemantauan visual serta prediksi dan koreksi kesalahan posisi robot secara efektif. Sistem pemantauan pada robot humanoid Krakatau FC sebelumnya terbatas pada penggunaan kamera, yang hanya memberikan perspektif visual serupa dengan apa yang dilihat oleh robot itu sendiri. Penelitian ini diarahkan untuk mengurangi kesalahan gerakan dengan menyajikan representasi robot dalam format tiga dimensi yang dapat dilihat dan dipahami secara real-time. Berdasarkan evaluasi melalui pengujian blackbox dan implementasi, dikonfirmasi bahwa setiap elemen sistem bekerja sesuai dengan ekspektasi.

COMBINATION OF AHP AND MAUT METHOD TO DETERMINE SCHOLARSHIP RECIPIENTS IN HIGHER EDUCATION (CASE STUDY: UNIVERSITAS TEKNOKRAT INDONESIA) Romdoni, Muhammad; Parjito, Parjito
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.1940

Universitas Teknokrat Indonesia is an educational institution located in the city of Bandar Lampung. Every new academic year a new student admission selection is carried out. Selection is carried out with two channels, namely regular and scholarship. One of the scholarship pathways is the Indonesia Smart Lecture Card (KIP-K). The acceptance of the scholarship pathway is done conventionally. This method certainly has obstacles, namely less effective time efficiency. The solution offered is through research by applying a combination of Analytical Hierarchy Process (AHP) and Multi Attribute Utility Theory (MAUT) methods to the Decision Support System. To assist in making decisions to determine prospective students who are eligible for KIP-K Scholarships, the right criteria are needed. The criteria used include economic status, achievement, parents' income, number of dependents, housing conditions, previous scholarships, parental assistance, organizational experience, test scores, and parents' status. The purpose of this research is to apply the AHP and MAUT methods in a decision support system that can assist the campus in determining scholarship recipients quickly, precisely and efficiently. The stages of this research are data collection, application of AHP and MAUT methods, and system implementation. Based on calculations carried out using a combination of AHP and MAUT methods, the highest preference value is Destia Putri with a value of 0.7791 and the lowest preference value is Pramutya Galuh 0.0444. Judging from the ranking results, it can be concluded that the combination of AHP and MAUT methods can be used to assist in decision making to determine prospective student recipients of KIP-K scholarships at Universitas Teknokrat Indonesia.

COMPARISON OF NAIVE BAYES AND RANDOM FOREST METHODS IN SENTIMENT ANALYSIS ON THE GETCONTACT APPLICATION Arisula, Juan Pala; Parjito, Parjito
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2004

The rapid growth in the use of social media and instant messaging platform apps has significantly changed the way people communicate. One of the most popular apps is GetContact, a platform focused on identifying the phone numbers of irresponsible people and reducing the impact of spam calls. In cases like this, sentiment analysis is important to understand user responses to the service. In performing sentiment analysis, there are two classification methods that will be used, namely the Naive Bayes and Random Forest methods. This research utilizes the SMOTE technique to handle data imbalance, and the results show that the application of SMOTE successfully improves classification accuracy. The Random Forest model performed better than Naive Bayes, with 80% accuracy, 84% precision, 77% recall, and 80% F1 score for positive sentiments, while Naive Bayes achieved 77% accuracy, 79% precision, 79% recall, and 79% F1 score. Although Random Forest is superior in precision, recall , and F1 score for positive sentiments, it performs almost on par with Naive Bayes in classifying negative sentiments, with 76% precision , 84% recall, and 80% F1 score for Random Forest, and 76% precision, 76% recall , and 76% F1 score for Naive Bayes. This shows that both models provide similar results in identifying negative sentiment overall.

Analisis Sentimen Komentar YouTube terhadap Kenaikan Tunjangan DPR RI menggunakan Naïve Bayes, SVM, dan Random Forest Dani, Jemmi Rama; Parjito, Parjito
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8513

The rise of digital technology encourages the public to actively voice their opinions through social media, including in response to political issues such as the policy on increasing the remuneration of the Indonesian House of Representatives (DPR RI). This research aims to analyze public sentiment towards this issue on the YouTube platform using a comparative approach with three Machine Learning algorithms: Naïve Bayes, Support Vector Machine, and Random Forest. The data was acquired from viewer comments via the YouTube Data Application Programming Interface (API), totaling 78,866 lines of comments collected from seven videos discussing the DPR RI controversy. The data collection process utilized the googleapiclient.discovery.build module with API version V3, where the API_Key served as the authentication key to access data from YouTube. The research stages included preprocessing for data cleaning, sentiment labeling based on the InSet Lexicon Based method, and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance in the data. The results show that before SMOTE application, the Support Vector Machine (SVM) model achieved the highest accuracy of 89%, followed by Random Forest at 81%, and Naïve Bayes at 62%. After applying SMOTE, the performance of all three models increased significantly, with SVM obtaining the highest accuracy of 93%, followed by Random Forest at 86%, and Naïve Bayes at 75%. For the positive class, SVM also demonstrated the best performance with a Precision value of 96%, Recall of 95%, and an F1-Score of 95%. Overall, the findings of this study confirm that SVM is superior in maintaining class balance in classification, both before and after SMOTE. The Machine Learning-based sentiment analysis approach is proven capable of providing a comprehensive overview of public opinion on political issues, while also offering important input for policymakers in formulating more transparent and responsive communication strategies.

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