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Journal : Building of Informatics, Technology and Science

Data Mining Clustering Korban Kejahatan Pelecehan Seksual dengan Kekerasan Berdasarkan Provinsi Menggunakan Metode AHC Sundari, Mitha Amelia; Pane, Rahmadhani; Rohani, Rohani
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3499

Abstract

Sexual harassment is one of the most common crimes in Indonesia recently. Acts of sexual harassment can occur in everyday life regardless of time, whether at work, on the street, or at home. Women are often the victims of sexual harassment, although men can experience the same. Perpetrators of sexual harassment can come from people we don't know, people who have hatred, even people we care about. Lack of religious and moral education, and technological developments that allow easy access to pornographic content are contributing factors to sexual harassment. To overcome this problem, fast action is needed in places where sexual harassment often occurs through socialization so that people are more vigilant when they are in these places. Apart from that, it is necessary to improve security in the area and provide consultation places such as psychologists. To identify places that are prone to sexual harassment in Indonesia, a data mining method is applied by utilizing previous data. The clustering method used is AHC using the complete linkage mode (longest distance) between the initial clusters. The final results of this research involve a manual process and the appropriate RapidMiner application, so that new clusters can be formed using RapidMiner. There are 5 provinces included in cluster 0, then there are 17 provinces in cluster 1, and 12 provinces in cluster 2
Analisis Perbandingan Sistem Pakar dalam Mendiagnosa Penyakit Limfoma Hodgkin Menggunakan Algoritma Teorema Bayes dan Certainty Factor Mahendra, Muklis; Pane, Rahmadhani; Rohani, Rohani
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3560

Abstract

Disease is a major challenge in the medical world. Accurate and timely diagnosis is a crucial key in disease management, including in cases of cancer. One type of cancer that affects the lymphatic system in the body is Hodgkin's lymphoma, which is also considered a rare disease. Typically, this disease occurs in adolescents and adults. Hodgkin's lymphoma requires serious treatment, although there are also cases of successful recovery. The importance of accurate and timely diagnosis in diagnosing Hodgkin's lymphoma is a critical factor in planning effective treatment and providing a favorable prognosis for patients. This study aims to perform a comparative evaluation of the Bayesian Theorem and Certainty Factor methods in diagnosing Hodgkin's lymphoma by comparing both methods. Diagnosing this disease is challenging for an expert due to the similarity of symptoms with other lymphoma diseases, which adds complexity. Therefore, this research provides an alternative to facilitate diagnosis by utilizing a system that can determine the level of certainty of a disease based on available data, including symptoms, expert values, and user values. After conducting research by comparing the two algorithms, Bayesian Theorem and Certainty Factor, various processing stages were implemented according to the established algorithm. The Bayesian Theorem algorithm yielded a result of 77.7%, while the Certainty Factor algorithm produced a higher value of 94.1%. The comparison between the Bayesian Theorem and Certainty Factor methods shows that the Certainty Factor method is more accurate in diagnosing Hodgkin's lymphoma and can be used in further research
Analisis Klasifikasi Sentimen Prediksi Rating Aplikasi Apple’s AppStore Dengan Menggunakan Metode Algoritma Random Forest Harahap, Armyka Pratama; Karim, Abdul; Rohani, Rohani
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.v6i4.6812

Abstract

Users of Apple's App Store applications are increasingly widespread among smartphone users. However, user responses to these apps vary widely. In addition, continuous developments in adding features and editing capabilities have led to the increasing complexity of using these applications. This research aims to analyze the sentiment of application users on Apple's App Store through reviews on the Google Play Store using the Random Forest method. This method was chosen to efficiently identify and group user responses into positive and negative categories. The dataset used in this study includes 5000 reviews, reflecting the diversity of opinions from actively participating users. The data preprocessing stage involves cleaning, case folding, tokenization, stopword removal, and lemmatization to ensure good data quality before sentiment analysis is carried out. Next, word weighting is carried out using the TF-IDF method to assign weight values to words that influence user sentiment. The research results show that the Random Forest method provides a high level of accuracy in analyzing user sentiment for Apple's App Store applications, with an accuracy of 86%, precision of 89%, recall of 81%, and f1-score of 85%. This research provides further understanding regarding user responses to Apple's App Store applications, and confirms the success of the Random Forest method in handling sentiment analysis on user review datasets on the Google Play Store.
Co-Authors A. A. Istri Agung Rai Sudiatmika Abdul Karim Ade Satrya Pamungkas Afriani, Nurafni Afrida Afrida Agastya, Dewandha Mas Agung, Rizki Nugraha Agus Muliadi Agustina, Elis Nurhayati Ahmad Habibullah, Imam Ahmad Syarif Akbar Ritonga, Ali Al Fajar, Mulya Ali Haidar Alhamidi Ali, Nizar Amiruddin Amiruddin Ammalia, Septiana Ade Ananda, Adelia Anas, Nirwana Anggraini, Nabila Anjar, Agus Arifin, Faizal Azizah, Mutia Nur Badila, Nala Bangun, Budianto Bianca, Tanya Bin Hasan, Suhendri Biroum, Raden Bte Salman, Nola Fibriyani Budi Raharjo Dada Suhaida Darwin P. Lubis, Darwin Desi Widianty Devi Susanti Dewangga, Lalu Singgih Sukma Dina Oktaria Dini Hariyati Adam Elfayetti Elfayetti Eliati, Eliati Eni Yuniastuti Erna Octavia, Erna Ernita, Mahdar Erpiani, Tika Fadillah, Fanny Suci Farha, Nudiya Afiya Farha, Nudiya Afiya Farida Farida Fatah Syukur, Fatah Fitra Delita, Fitra Fitrah, Qifyanti Fitri Indah Sari Fransita, Sam Randa Ghazali Matondang, Muhammad Farouq Gunawan, Yogi Hadi Rianto Handayani, Fitriya Handayani, Sri Harahap, Armyka Pratama Harahap, Nur Salimah Harahap, Yulia Fatma Harniz, Innat Adlan Adillah Hasibuan, Elysa Rohayani hasyim, hasyim Hasyim, Hasyim Hendripides, Hendripides Heppy Nidia, Wira Herdi Herdi Heri Hadi Saputra Hikmawati Hikmawati Hrp, Nurlina Ariani Humairoh Saidah Husnarika Febriani, Husnarika I Dewa Gede Jaya Negara I Wayan Merta I Wayan Suastra I Wayan Suteja I Wayan Yasa, I Wayan IAO Suwati Sideman Ida Ayu Oka Suwati Sideman Ida Ayu Putu Sri Widnyani Ifada Retno Ekaningrum Isbat, Imam Hanafi Ismiaton, Laela Iwan Purnama JAYANEGARA, I DEWA GEDE Juledi, Angga Putra Juliandari, Kriska Afri Jumaiyah, Wati Jumarti, Jumarti Jusman, Ikhsan Amar Kamarlin Pinem Karyawan, I Dewa Made Alit Kesumawati, Tri Indah Kesumawati, Tri Indah Ketut Suma Khairuna Khairuna Kholifatur Rosydah, Awal Nur Kirana, Fira Kurniawati, Eka Lastfitriani, Hellen Lestari, Susan Lia Agustina, Lia Luh Putu Ratna Sundari M. Fahli Zatrahadi Made Mahendra Mahendra, Made Mahendra, Muklis Maman, Maman Manalu, Kartika Mariataun, Mariataun Marjani, Oktavia Erlina Marlinang Sitompul Masita, Maya Matsun Mbina Pinem Meilinda Suriani Moad, Moad Mona Adria Wirda Muhammad Halmi Dar Muhibbin, Muhibbin Naskah, Naskah Nasution, Depi Yanti Nasution, Marnis Nasution, Putri Amalia Saqina Nazliah, Rahmi Nia Anggraini Niko Aryo Saputra Nurbaiti, Syarifah Nurfirdayanti, Nurfirdayanti Nurfitriani Nurfitriani, Nurfitriani Nurjannah, Suci Nurjannah Nurmalia, Nurmalia Padmaningtyas, Dewi Anggraini Pane, Rahmadhani Panjaitan, Devi Hertina Pasaribu, Jupita Sari Pasaribu, Laili Habibah Pratiwi, Cesa Septiana Pratiwi, Citra Aulia Purwangsa, Herdi putri, amanda afriza Rahayu, Yayuk Rahliadi, Royim Rahliyadi, Royim Rahma, Indah Fitria Rahmadani Br Solin, Sri Rahmadi, M. Taufik Rahmani, Dian Ayu Rahmawati, Ati RANI RANI, RANI Ratna Nila Puspitasari, Ratna Nila Ratna Yuniarti Rehan, Rehan Rizki Amelia Rohani Rohani, Fety Novianty, Hemafitria, Rohani, Rohani, Fety Novianty, Hemafitria, Rohani, Rohani, Sofia Rohil, Lalu M. Rokyal Harjanty Rube'i, Muhammad Anwar S. Sugiharto Safira, Mirna Salehuddin, Salehuddin Salmiah Salmiah Salsabila, Fera Fitri Sanusi, Andi Saputri, Arni Nur Isma Sari Hernawati Sari, Eka Suci Indria Shidiq, Ngarifin Sideman, IAO Suwati Sihombing, Pirma Juragan Rezky Siregar, Nurul Jannah Siregar, Shara Jumiati Siti Nurhidayati Sitorus, Sahat Parulian Solin, Raslina Sopar Sri Adelila Sari Sri Hamdyani Srikartikowati, Srikartikowati Suaeb, Suaeb Suarman Sundari, Mitha Amelia Suriyani Suriyani, Suriyani Suryani, Ira Syarif Firmansyah Trisna, Nabilla Fairus Izzati Eka Ulfa, Syarifah Widya Umar Faruq Utami, Febriani Putri Vebrianto, Rian Ventura, Ewaldus Odo Wahab Wahab Wardana, Tara Wibowo, Soesilo Wijayanti, Tri Sari YENI RIZAL Yenny, Novida Yufrizal Yufrizal, Yufrizal Yuliananingsih M, Yuliananingsih Yuliananingsih Yuliananingsih, Yuliananingsih Yuliastrin, Adisti Yunitri, Ninik Yusmanijar, Yusmanijar Zahratunnisa, Elvira Zakiah Zakiah Zuldafrial Zuldafrial, Zuldafrial