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All Journal Jurnal Simantec Jurnal Riset Informatika Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] GANDRUNG: Jurnal Pengabdian Kepada Masyarakat Jurnal Mahasiswa Sistem Informasi (JMSI) Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Jurnal Penelitian Rumpun Ilmu Teknik JUSTIFY : Jurnal Sistem Informasi Ibrahimy Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Journal Of Computer Science And Technology JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Jurnal Ilmiah Multidisiplin Nusantara Gudang Jurnal Multidisiplin Ilmu Jurnal Ilmiah Research Student Jurnal Sains Student Research JURNAL ILMIAH PENELITIAN MAHASISWA Jurnal Imiah Pengabdian Pada Masyarakat (JIPM) Jurnal Ilmiah Multidisiplin Ilmu Jurnal Riset Teknik Komputer Jurnal Pengabdian Masyarakat Jurnal Riset Sistem Informasi Jurnal Padamu Negeri Jurnal Pendidikan Sains dan Teknologi Terapan Jurnal Sistem Informasi Karya Anak Bangsa Jurnal Teras Pengabdian Masyarakat Jurnal Pengabdian Indonesia Abhakte
Zaehol Fatah
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ANALISIS PENGELOMPOKAN DATA NILAI SISWA UNTUKMENENTUKAN SISWA BERPRESTASI MENGGUNAKAN METODE CLUSTERING K- MEANS Mochammad Syukron Ramadani; Zaehol Fatah
Jurnal Riset Sistem Informasi Vol. 1 No. 4 (2024): Oktober : Jurnal Riset Sistem Informasi
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/hq2bsy84

Abstract

Identifying high-achieving students is a critical step in evaluating learning outcomes to enhance the quality of education. This study aims to analyze the clustering of student grade data using the K-Means Clustering method to identify groups of high-achieving students. The K-Means method is utilized due to its effectiveness in grouping data based on value similarity. The data used in this study consist of students' academic scores across various subjects. The research stages include data collection, preprocessing, applying the K-Means algorithm, and validating the clustering results. The results show that the K-Means method successfully grouped students into several categories, such as high-achieving, moderate-achieving, and low-achieving students. The clustering analysis indicates that high-achieving students exhibit consistent performance across all subjects, whereas low-achieving students tend to show significant variations in their scores. This method also provides data visualization that helps schools make informed decisions to improve student performance. Thus, the implementation of the K-Means method in clustering student grade data can serve as an effective and efficient approach to support evaluation processes and data-driven decision-making.
IMPLEMENTASI DATA MINING UNTUK MENGANALISA POLA PENJUALAN MENGGUNAKAN METODE K-MEANS PADA CV. HAVAS P2S2 Alfan Jamil; Zaehol Fatah
Jurnal Riset Sistem Informasi Vol. 1 No. 4 (2024): Oktober : Jurnal Riset Sistem Informasi
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/rxnjxb68

Abstract

Sales are an important aspect in the continuity of company operations, including CV. Hafas P2S2 which is engaged in the distribution of bottled drinking water products. In order to increase the effectiveness of marketing strategies and stock management, it is important to analyze sales patterns that occur. This research aims to implement data mining techniques using the K-means method to analyze sales patterns at CV. Memorize P2S2. The K-means method was chosen because of its ability to find associative relationships between items that are often purchased together in transactions. The data used in this research involves sales information recorded in the company system. The results of applying the K-means algorithm show that there are certain combinations of items that are often sold together, which provides valuable insights for companies in terms of stock management and marketing strategies. It is hoped that these findings can help CV. Hafas P2S2 in improving operational efficiency and maximizing profit potential by better understanding customer demand patterns. Thus, the implementation of data mining through the K-means method makes a significant contribution to data-based decision making in the company's sales sector.
PENGELOMPOKAN DATA NILAI SISWA MADRASAH TA’HILIYAH MENGGUNAKAN METODE K-MEANS CLUSTERING Fahrillah Fahrillah; Zaehol Fatah
Jurnal Riset Sistem Informasi Vol. 2 No. 1 (2025): Januari : Jurnal Riset Sistem Informasi
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/0v1pkz05

Abstract

Data mining, or data mining is the process of collecting and processing data to extract important information. The stages in the data mining process are useful for finding a particular pattern from a large amount of assessment data. This goal is to find out and form student data clusters based on grades so that they become a cluster, so that the results of student clusters can be a reference in improving student grades in the next learning process. The results of the evaluation and assessment of students are carried out by teaching staff or teachers in conducting assessments during the learning process. In the learning process there are 2 assessment categories, namely UTS and UAS student grades. The results of grouping student grade data using the K-Means clustering method show that based on the results of student data clusters in one semester, cluster 0 is obtained with 7 students, cluster 1 is 3. The results of testing using rapid miner show that there are 7 students who have grades with a good average and there are 3 students with a poor average grade.
PREDIKSI NILAI INDEKS EKSPOR SUSU SEGAR DI INDONESIA MENGGUNAKAN ALGORITMA BACKPROPAGATION Holil Asy’ari; Zaehol Fatah
Jurnal Riset Sistem Informasi Vol. 2 No. 1 (2025): Januari : Jurnal Riset Sistem Informasi
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/46fpc085

Abstract

The results of research on the value of exports in Indonesia can be concluded that the architectural model can make predictions with 100% accuracy with a short training time. In addition, by looking at the results of testing on architecture, it can be seen that both the speed and the prediction results, it can be concluded that the value of exports in Indonesia is increasingly declining. For future research, research should use a different algorithm or the Backpropagation algorithm can be optimized with other algorithms, such as conjugate gradient and so on, that is, the research was first carried out using the backpropagation algorithm, then after knowing the results, calculations were carried out with the algorithm. other. After that, a comparison is made between the results of backpropagation with the results of other algorithms. Images or graphs of each algorithm used, or a graphic image of the comparison of the previous data with the data that has been generated using the algorithm.
ANALISIS POLA KEHADIRAN MAHASISWA MENGGUNAKANALGORITMA DECISION TREE Mu’tashim Billah Rahman; Zaehol Fatah
Jurnal Riset Sistem Informasi Vol. 2 No. 1 (2025): Januari : Jurnal Riset Sistem Informasi
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/6z8kc143

Abstract

Student attendance in lectures plays a crucial role in academic achievement and the quality of learning. The Decision Tree algorithm is used to analyze student attendance patterns with a dataset containing 6,607 entries from Kaggle, comprising 20 related attributes. Using RapidMiner, the analysis process includes data splitting, model building, and performance evaluation. The model achieved 49.96% accuracy, with the best performance in the "Medium" class (50.40% precision, 98.12% recall) but showed weaknesses in the "High" and "Low" classes. These results highlight the importance of data-driven approaches to designing effective strategies, such as rescheduling or improving teaching methods, to enhance student participation.
ALGORITMA K-MEANS CLUSTERING UNTUK MENENTUKAN SISWA UNGGULAN BERDASARKAN HASIL UJIAN DI SEKOLAH Ainul Fadil; Zaehol Fatah
Jurnal Riset Sistem Informasi Vol. 2 No. 1 (2025): Januari : Jurnal Riset Sistem Informasi
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/p26gcf27

Abstract

Determining classes for outstanding students based on exam results is a crucial step in promoting the improvement of learning quality. This study applied a data mining method using the K-Means Clustering algorithm to group students based on their exam results. The process includes collecting exam score data, preprocessing the data, and applying the K-Means algorithm to form several student groups based on their achievement levels. Through this algorithm, students are clustered into groups with similar characteristics, such as excellent, average, and those requiring more attention. The study's results indicate that the K-Means Clustering approach can provide an accurate representation of the distribution of student abilities, serving as a basis for designing more effective and equitable learning strategies. This implementation is expected to help schools identify students' potential more objectively and enhance overall educational quality.
Penerapan Decision Trees dalam Mendeteksi Pola Tidur Sehat Berdasarkan Kebiasaan Gaya Hidup Imam Nawawi; Zaehol Fatah
JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Vol. 2 No. 4 (2024): Oktober
Publisher : CV. ALIM'SPUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jiti.v2i4.969

Abstract

A good sleep pattern is very important for our body's health both physically and mentally, while lifestyle habits such as physical activity and diet play a big role in influencing sleep quality. By using a decision tree, researchers aim to predict whether we have a healthy sleep pattern or not based on lifestyle. Healthy sleep patterns are regular and quality sleep habits to maintain our physical health. Healthy sleep patterns generally involve sleeping 8 hours – 9 hours per night, having a regular and consistent sleep time. The decision tree model was chosen because of the decision tree's ability to provide accurate predictions and produce rules that are easy to understand. This model can help us raise awareness of the importance of a healthy lifestyle in maintaining sleep quality.
PREDIKSI RISIKO DEMAM BERDARAH MENGGUNAKAN DECISION TREE BERDASARKAN GEJALA KLINIS DAN DATA LABORATORIUM M. Fazlur Rahman Assauqi; Zaehol Fatah
JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Vol. 2 No. 4 (2024): Oktober
Publisher : CV. ALIM'SPUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jiti.v2i4.972

Abstract

Dengue Hemorrhagic Fever (DHF) is a disease caused by the Dengue virus and has a significant impact on public health, especially in tropical areas. Early diagnosis and prediction of DHF risk are essential to prevent complications and improve medical care. This study aims to develop a DHF risk prediction model using the Decision Tree method based on clinical symptoms and laboratory data. The data used include symptoms such as fever, joint pain, rash, and laboratory results such as platelet count and hematocrit. The Decision Tree model was chosen because of its ability to handle data with various variables and provide easy-to-understand interpretations. The research data were taken from patients diagnosed with DHF in several hospitals during a certain period. The dataset was then analyzed to find relevant patterns that could predict a high risk of DHF. The model training and testing process was carried out using cross-validation techniques to ensure prediction accuracy. The results showed that the Decision Tree model had an accuracy rate of 96.95% and consistent results from cross-validation which produced an average accuracy of 92.8%,, with good sensitivity and specificity in predicting DHF risk based on a combination of clinical symptoms and laboratory data. Factors such as low platelet count and fever symptoms lasting more than three days were found to be significant predictive variables. In conclusion, this Decision Tree model has the potential to be used as a tool in early prediction of DHF risk, which can help medical personnel in clinical decision making and patient management. Further development can be done by adding other variables such as epidemiological data to improve model performance.
Analisis Sentimen Access by Bus Kota se-Indonesia Menggunakan Metode K-Nearest Neighbors M. Andrik Muqorrobin P; Zaehol Fatah
JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Vol. 3 No. 1 (2025): Januari
Publisher : CV. ALIM'SPUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jiti.v3i1.994

Abstract

Data mining atau penambangan data merupakan proses pengumpulan dan pengolahan data untuk mengekstrak informasi penting. Metode data mining K-Nearest Neighbor dapat menganalisis pada aplikasi Redbus. RedBus merupakan salah satu aplikasi resmi pembelian tiket bus kota di Indonesia. Permasalahan yang muncul setelah pembaruan aplikasi RedBus adalah bertambahnya ulasan bintang satu yang menyatakan bahwa versi terbaru tidak sesuai dengan versi sebelumnya. Data Mining yang dugunakan untuk menganalisis sentimen Access by Bus Kota di seluruh Indonesia menggunakan metode K-Nearest Neighbors. Data yang digunakan adalah data yang diperoleh dari ulasan pengguna aplikasi redBus selama satu bulan terhitung dari tanggal 20 September 2024 sampai dengan 20 Oktober 2024 dengan total 1291 ulasan. Analisis sentimen pada penelitian ini menggunakan metode K-Nearest Neighbors melalui bahasa pemrograman Python. Hasil penelitian menunjukkan bahwa kinerja terbaik pada percobaan dengan pembagian data latih dan data uji, serta nilai k yang bervariasi diperoleh pada percobaan dengan pembagian 90% data latih, 10% data uji dan menggunakan nilai k = 5 dengan nilai akurasi, presisi, dan recall masing-masing sebesar 90,23%; dan nilai recall sebesar 72,38%. Klasifikasi sentimen dengan model terbaik menggunakan parameter k = 3 menghasilkan 79,26% sentimen positif, 17,25% sentimen netral, dan 3,49% sentimen negatif.
Analisis Pengaruh Jenis Buku Terhadap Minat Baca Mahasiswa di Perpustakaan Ibrahimy dengan Algoritma K-Means Clustering Mahmudi Mahmudi; Zaehol Fatah
JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Vol. 3 No. 1 (2025): Januari
Publisher : CV. ALIM'SPUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jiti.v3i1.1013

Abstract

In today's digital era, students' interest in reading seems to be declining, particularly in literacy activities aimed at enhancing knowledge. This issue has become a concern in efforts to foster a reading culture among students. This study aims to analyze and describe the types of books that can influence students' reading interest. Data were collected through student evaluations, lecturers' opinions, and librarians' perspectives. The data collection methods included questionnaires, observations, and interviews, with data analysis conducted through reduction processes. The study results highlight four main points: 1) The types of books that attract students' interest include fiction and non-fiction books. 2) External factors influencing reading interest include the environment, support from lecturers, and available facilities. 3) From librarians' perspectives, students' reading interest is affected by curiosity, available facilities, and academic assignments. 4) Efforts to enhance students' reading interest can be carried out through activities such as library visit competitions and book review contests. In conclusion, two types of books—fiction and non-fiction—can influence students' reading interest. A survey of 100 students revealed that 75% preferred fiction books, while the remaining 25% favored non-fiction books.
Co-Authors Abdul Hadi Abdur Rohman Nurut Toyyibin Abrori, Syariful Ach. Zubairi Achmad Fathoni Verdian Afcharina Diniyil Muhlisin Afrizal Rizqy Pratama Ahmad Homaidi Ahmad Muflih Wafir Ahmad Syahril Lail Ahmad Wahyu Fernando Ahmed Arifi Hilman Rahman Ahsin Ilallah Ainul Fadil Aisyah Putri Sabrina Akhlis Munazilin Alfan Jamil Alfi Fahira Salsabila Alfi Khairunnisa Alfina Damayanti Alfiyah Aurella Alifan Ibrohim Alifia Rosa Firdausiah Alviatur Rizqiyah Amelia Ismatul Hawa Ammar Farisi Anang Maulana Zulfa Angeli Dwiyanti Nur’azizah Anisa Anisa Anwar Anas Anzori Arif Ferdiansyah audiatul jinan Auliya Apriliana Aviatus Sholiha Bagas Wira Yuda Basmalia Bina Cahya Pamungkas, ihya16092002 Citra Nursihah Danil Bahroni Della Natasya Diana Uzlifatul Khairu Ummah Dila Puspita Dewi Diva Maulana Dwi Alya Putri Arifany Dzakwan Rohmatul Hanif Elvi Nazulia Rahma Elvina Eldiavani Epariani Erinia Dzikrotul Kharimah Fahrillah Fahrillah Faqih Nur Rahman Fatimah Isa Auliya Fatma Nur Afifah Faza Qori Aina Fikri Rostina Firda Wati Husaini Kulsum Fitri Elvi Karisma Fitria Ayu Ulandari Hafidz, M. Fajar Hasna Ruhmaniatin Herlinatus Safira Muasolli Hermanto , Hijrah Hijriah Holida Izzatilla Holil Asy’ari Huday, Ahmad Ifan Farimulyadi Ifan Prasetyariansyah Ifqy Ahmad Fahrizal iin, Nur Inayah Ika Indah Khasanah ila, Sufatun Aila Ilham Rafi Jawara Ilham Rafiqi Imam Nawawi Imelda Valentina Octavia Indah Novita Sari Iqbal Ainul Yaqin Irfansyah, Khairullah Irham, Muhammad Nazril Irma Yunita Islamiyatul Addewiyah Ismawati Ismawati Ismawati Ivana Dwikartika Sari j-sika Jarot Dwi Jarot Dwi Prasetyo Jefri Jefri Jesika Maya Nur Islami Kayyisah Fakhirah Kevin Riyas Robbani Khairul Anam Khozaimah Dian Islami Komarul Imam Laila Devi Sari LAILATUL FITRIYAH Lailatul Risqia Lailatus Syarifah Lailatussyarifah Lina Sosiana Lisa Novia Ramdani Lubebetun Nafisa Lukman Fakih Lukman Fakih Lidimilah Luluk Nuril Mukarromah Lutfiana , Nurisma Lutfiyatul F Anas Lu’luul Maulidya Nova M. Andrik Muqorrobin P M. Andrik Muqorrobin Pratama M. Fazlur Rahman Assauqi Maharani Rahmatul Hanani Mahmudi Mahmudi Mamluatur Rizkiyatun Nafiah Manda Nuria Suhailatin Najwa Maruf Ubaidillah Maryana Mashuri, Ahmad Meliana Khamisah Mifta Wilda Al -Aluf Miftahul Arif Aldi Milka Afifah Rahmatillah Mochammad Rofi Mochammad Syukron Ramadani Moh. Agus Efendi Moh. Baha’Uddin Moh. Syahrul Iskandar Moh. Zaini Romly Mohamad Faezal Fauzan Nanda Mohammad Alfian Husni Mubarok Mohammad Farhan Fatah Muchammad Atfal Nur Afil Muflihatul Hasanah Muftiyah Zakiyah Muhamad Auliya Muhamad Ilhan mansiz Muhammad Al Madany Muhammad Faidhurrahman Wahid Muhammad Hanif Zaky Ubaidillah Muhammad Hasan Muhammad Robitul Umam Muhammad Trisnawadi Ismardani Mutmainnah Ilmiatul Faidah Muyessiroh Muzayyana, Muzayyana Mu’tashim Billah Rahman Nabila Khansa Nabila Sofia Az-zahra Nadia Selvi Ramadhani Nafisatul Insiyah Naqibuzzahidin Naufal Arif Maulana Nur Aida NUR AINI Nur Azise Nur Dina Kamelia Nur Laili Mukarromah Nur Rizatul Mufidah Nur Sahila Chapsah Nur Saputra, Zuhrian Nurin Naimah Nurisma Lutfiana Prastika Buya Hakim Putri Anindya Damayanti Qittratul Ameliatus Qurratul Aini Rafi Jawara, Ilham Raihan Asriel Afandi Ratu Maulidia Anggraini Regina Izza Aofkarina Riatul Jannah Rifki Dwi Saputra Risma Alfiatul Karima Risqiatus Syarifah Risqiyati Amilia Ningsih Rita Irawati rizka, Rizka Aprilia Ningsih Rizki Hidayaturrochman Rosita Natania Maulani Rudi Ananta Al Hidayah Ruqoyyatul Widad Ruwaida Khollatil Widat Safitri Nurul Qomariyah Sagita Maesarah Septi Camelia Ulfa Sidra Al Zahro Sinta Bella Sinta Dewi Anggraeni Siti Aysatin Rodia Siti Imroatul Jannah Siti Kholifah Siti Maghfiroh Siti Nabilatul Hoiroh Siti Nur Azizah Siti Romlah Siti Sulaiha Sitti Ainur Rofiqotul Anisa Sofi Naila Nuriyazih Sofyan, Moh Sofyan Alfandi SU'AYDI, AHMAD SU'AYDI Suci Mulianingsih Sukiman Eki Putra Sulistia Wardani Supri Arrohman Syaiful Hasan Abdullah Syirva Nada Fidya Tadzkirotul Latifah Taufik Saleh Ubeitul Maltuf Ulvi Munawaroh Ummi Fadlilatuz Zakiyah Ummil Mahfudoh Ummul Khoirun Fitriyah Uny Khafifah USWATUN HASANAH Wafi Riga Ramadhani Wafi, Wafi Wardatul Gufronia Wildatul Hasanah Winda Yanti Umami Wiwik Handayani Wulan Shelfiana Kamil Yeni nur hasanah Yua Isman Islam Yulina Sari Zahrafil Jannah Zainur Rahman Zakiyatus Solehah