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All Journal Jurnal Ilmiah Informatika Komputer Teknika Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan CESS (Journal of Computer Engineering, System and Science) Jurnal CoreIT JURNAL KAJIAN TEKNIK ELEKTRO JTAM (Jurnal Teori dan Aplikasi Matematika) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi INTECOMS: Journal of Information Technology and Computer Science KACANEGARA Jurnal Pengabdian pada Masyarakat Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) IJID (International Journal on Informatics for Development) JURIKOM (Jurnal Riset Komputer) Jurnal Tekno Kompak TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Indonesian Journal of Electrical Engineering and Computer Science Bubungan Tinggi: Jurnal Pengabdian Masyarakat Jurnal Manajemen Informatika Jayakarta International Journal Software Engineering and Computer Science (IJSECS) Berdikari : Jurnal Pengabdian kepada Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Technology and Informatics Insight Journal KAMI MENGABDI Journal of Data Science Theory and Application Journal of Digital Business and Management Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Edusight International Journal of Multidisciplinary Studies (EIJOMS) International Journal of Law Social Sciences and Management Computer Journal
Tundo, Tundo
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MODEL ALGORITMA KNN UNTUK PREDIKSI KELULUSAN MAHASISWA STIKOM CKI Tiara Ratu Alifia; Tundo Tundo; Muhammad Syazidan; Faldo Satria
Jurnal Ilmiah Informatika Komputer Vol 29, No 2 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i2.11803

Abstract

This study develops a student graduation prediction model using the K-Nearest Neighbor (KNN) algorithm, considering variables such as age, Grade Point Average (GPA), number of Credits Earned (CE), participation in TOEFL tests, seminar activities, and participation in internships. Data from 80 students in the computer engineering and information systems programs at STIKOM Cipta Karya Informatika were analyzed to train and test the model. The results show that the KNN model with K=3, K=4, and K=5 produces a prediction accuracy of 66,67%. GPA and the number of credits earned significantly influence graduation, while participation in internships and TOEFL tests also contribute. Seminar certificates and age have a lower impact. These findings indicate that the KNN algorithm is effective for predicting student graduation, providing insights for educational institutions to enhance academic programs and student development.
Penerapan Algoritma Naive Bayes Dalam Mengetahui Pola Pengguna Keluarga Berencana Pada Tempat Praktek Mandiri Bidan (TPMB) Lilik Faiqoh Sugiono, Sugiono; Marliani, Tiara; Sarimole, Frencis Matheos; Tundo, Tundo
CESS (Journal of Computer Engineering, System and Science) Vol. 9 No. 2 (2024): July 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i2.61406

Abstract

Seiring kemajuan teknologi dan informasi yang semakin berkembang, dan menjadikan masyarakat paham akan pentingnya segala informasi, termasuk tentang Keluarga Berencana atau KB. Berdasarkan observasi dan wawancara dengan bidan Lilik Faiqoh bahwa yang menjadi masalah kurangnya penyuluhan terhadap masyarakat, supaya masyarakat paham apa saja alat kontrasepsi yang ada di TPMB Lilik Faiqoh Jakarta Timur. Untuk mengatasi masalah tersebut, maka Algoritma Naive Bayes merupakan salah satu algoritma machine learning yang dapat digunakan untuk mengklasifikasikan data. Tujuan dari penelitian ini adalah untuk menentukan penerapan Algoritma Naive Bayes dalam mengetahui pola pengguna Keluarga Berencana pada TPMB Lilik Faiqoh dengan mencakup identifikasi jenis kontrasepsi (KB) yang paling sering digunakan. Kemudian untuk data Keluarga Berencana ini akan dilakukan dengan proses penerapan metode CRISP-DM. Penelitian ini diharapkan dapat meningkatkan layanan TPMB Lilik Faiqoh dan memberikan manfaat yang lebih besar bagi masyarakat setempat dalam hal penyediaan layanan kesehatan.
Assessment of the President of BEM Using the Weighted Product Method at XYZ University Tundo, Tundo; Nugroho, Agung Yuliyanto; Saidah, Andi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 1 (2025): June 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i1.21075

Abstract

The election of the BEM President is a hereditary tradition at XYZ University every year. This election was carried out to find a leader who has a firm personality and broad insight. As the number of students at XYZ University increased, we doubled the election using the Weighted Product (WP) method with the conditions that we had determined with the campus. So we are sure that this method will produce the leaders we expect, and also in this way the campus automatically saves budget for voting or direct elections. The WP method which is quantitative in decision making, the WP method uses multiplication to link attribute ratings, where the rating of each attribute must be raised to the first power of the attribute weight in question. By applying the WP method to decision support system, then implementing it into a ranking system, it will produce students who deserve to become BEM in the next period. There is a WP method at XYZ University in order to get a BEM President who meets the criteria we set. Where the existing criteria consist of TPA criteria, Liveliness, Commitment, GPA, Absent, and Age. After calculating using the WP method, it was found that the strongest student who deserved to be president of BEM was Siti Munawaroh who was ranked first. The results of the recommended method by conducting a questionnaire to the BEM management by producing an accuracy of 0.01356.
Forecasting Beef Production with Comparison of Linear Regression and DMA Methods Based on n-th Ordo 3 Tundo, Tundo; Yel, Mesra Betty; Nugroho, Agung Yuliyanto
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.24706

Abstract

Beef is considered a high-value commodity because it is an important food source of protein. Interest in beef is increasing along with increasing people's incomes and awareness of the importance of fulfilling nutrition. Demand for beef is expected to continue to increase. According to the Central Statistics Agency (CSA), beef production in Jakarta shows an increasing trend every year. In the last 10 years, beef production has increased significantly, but in 2020 there was a decrease in production of 7,240.68 tons due to the lockdown due to the corona virus outbreak. After that, in 2021, production reached 16,381.81 tons and will continue to increase in 2022 and 2023. Based on the above phenomenon, the aim of this research is to support the success and sustainability of the beef industry by ensuring that supply matches demand, resources are used optimally, and risks can be managed well. To predict beef production, an accurate method, model or approach is needed. One way to predict beef production in Jakarta is to use the Linear Regression and Double Moving Average (DMA) methodsThe way the Linear Regression and DMA methods work is to forecast based on concepts and properties. The concepts and properties of Linear Regression are models, functions, estimates and forecasting results, while DMA performs time series analysis based on moving averages. After analysis using MAPE, it was found that the algorithm that had the smallest error value was the linear regression algorithm with a percentage for the monthly period of 15% while for the year period it was 17% compared to DMA. So in this case it would be very appropriate to use the Linear Regression method from the error values obtained.
Evaluasi Kepuasan Pelanggan terhadap Kendaraan Motor Vario Menggunakan Metode Simple Additive Weighting (SAW) Purnasiwi, Rona Guines; Tundo, Tundo
Computer Journal Vol. 3 No. 2 (2025): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v3i2.323

Abstract

This study aims to measure the satisfaction level of Honda Vario motorcycle owners by applying the Simple Additive Weighting (SAW) method. SAW was chosen to evaluate various factors, including price, engine performance, fuel efficiency, safety, and riding comfort. A survey was conducted involving 100 Honda Vario owners to gain insights into their experiences. The results indicate that 18 participants were very satisfied, 39 satisfied, 30 moderately satisfied, and 13 dissatisfied. Comfort and fuel efficiency received the highest appreciation among users. Although some respondents mentioned shortcomings in certain features, most still prefer Honda Vario for daily transportation. These findings can assist manufacturers in understanding customer expectations and considering improvements in product quality as well as after-sales service. For prospective buyers, the results offer useful information to help match their choices with personal needs. By using the SAW approach, the evaluation process becomes more objective, supporting better decisions for both users and manufacturers.
Forecasting Roof Tiles Production with Comparison of SMA and DMA Methods Based on n-th Ordo 2 and 4 Yel, Mesra Betty; Tundo, Tundo; Arinal, Veri
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.22225

Abstract

This research aims to predict roof tile production trends at one of the roof tile companies in Kebumen to assist company management in determining and providing management recommendations for the tile production that occurs. A comparison of Single Moving Average (SMA) and Double Moving Average (DMA) Forecasting methods was used to better accommodate trends in roof tile production data optimally. Where the forecast is presented for several steps ahead, and is equipped with a value measuring the accuracy of the forecast using Mean Absolute Percentage Error (MAPE), on roof tile production transaction data over 60 months, namely January-December 2019 to January-December 2023 to produce a monthly forecast for predicting roof tile production with n-th ordo 2 and 4. The total sample of training data processed was 1,415,987 records which were roof tile production transaction data, as well as data in January 2024 as test data (to test the accuracy of the forecast). The results of testing the forecast results produced a MAPE calculation of 6.6% for SMA with n-th ordo 2, while for n-th ordo 4 it was 7.2%. The MAPE value for DMA is 6.3% for n-th ordo 2, while for n-th ordo 4 it is 8.2%, which means the accuracy level is very good, namely above 90%. Based on the MAPE results obtained, the DMA method with n-th ordo 2 is a suitable method for carrying out periodic forecasting for roof tile companies in carrying out the production process to maintain stability and avoid unexpected events.
Implementasi Regresi Linear dan Single Exponential Smoothing Dalam Prediksi Harga Saham ANTM Paidi, Imam; Tundo, Tundo; Rasiban, Rasiban; Suropati, Untung
TEKNOKOM Vol. 7 No. 2 (2024): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v7i2.222

Abstract

This study focuses on using linear regression and single exponential smoothing (SES) models to predict the share price of PT Aneka Tambang Tbk (ANTM). Data from Yahoo! Finance covering the period from 2005 to 2023 is used. The linear regression model establishes a relationship between the current and previous stock prices, while the SES model smoothes out fluctuations and captures shortterm trends. The findings reveal that both models are highly accurate in predicting ANTM stock prices. However, the SES model is less consistent in capturing shortterm trends, suggesting its effectiveness lies in capturing seasonal and short-term trends in the ANTM stock price data. This research is significant as it contributes to the development of accurate and reliable stock price prediction models, which can assist investors and players in the capital market in making informed investment decisions. The results also provide a foundation for future research on applying more complex and sophisticated forecasting models for stock price prediction.
Optimisasi Penjadwalan Kegiatan Guru pada SMK IDN Boarding School Jonggol dengan Penerapan Algoritma Genetika Nuradi, Fahmi; Tundo, Tundo; Mulyana, Dadang Iskandar; Lestari, Sri
TEKNOKOM Vol. 7 No. 2 (2024): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v7i2.223

Abstract

This document describes guidelines for Authors in writing an article in JIMIK. This abstract section should Activity scheduling is a crucial aspect of educational management, especially in school environments with many activities and limited resources. At IDN Boarding School Vocational School, the challenges in scheduling teacher activities become increasingly complex as the number of subjects, extracurricular activities and limited resources such as space and time increase. Manual scheduling methods using spreadsheets such as Microsoft Excel take a long time and are prone to human error. This research proposes the application of a Genetic Algorithm to optimize the scheduling of teacher activities at the IDN Boarding School Vocational School. The Genetic Algorithm was chosen because of its ability to find optimal solutions through selection, crossover and mutation processes. This algorithm is able to handle various constraints in scheduling, both hard constraints (constraints that must be obeyed) and soft constraints (constraints that are desired but not mandatory). The aim of this research is to develop an automatic scheduling system that can reduce delays and the risk of errors in preparing schedules, adjust schedules quickly when sudden changes occur, and distribute teacher workload more evenly. The research results show that the application of a Genetic Algorithm can produce a more efficient and effective schedule compared to manual methods, by minimizing schedule conflicts, optimizing space use, and ensuring a more balanced distribution of teacher workload. This research not only provides a solution to scheduling problems at the IDN Boarding School Vocational School, but can also be adapted and applied to other educational institutions. Thus, this research makes a real contribution to improving the quality of educational management and teaching and learning processes in Indonesia.
Prediksi Tingkat Stres Pada Mahasiswa UNUGHA Cilacap Menggunakan Algoritma K-Nearest Neighbor Wafiqi, Achmad Ulul Azmi; Tundo, Tundo; James, Bobby Arvian; Ramadhan, Abhirama Huga; Nizar, Amin
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.v18i2.3933

Abstract

Penelitian ini bertujuan untuk meningkatkan kewarasan mahasiswa agar dapat menyelesaikan studi belajar tanpa adanya stres yang melanda, khususnya bagi mahasiswa UNUGHA Cilacap. Langkah yang digunakan yaitu, dengan cara memprediksi tingkat stres mahasiswa dengan menggunakan Algoritma K-Nearest Neighbor (KNN) berdasarkan faktor-faktor yang mempengaruhi keadaan psikologis mereka, seperti tekanan akademis, kesimbangan kehidupan, dan faktor-faktor psikologis lainnya. Dalam penelitian ini dipengaruhi oleh faktor Kebiasaan Studi, Waktu Tidur, Aktivitas Fisik, Kesehatan Fisik, Tingkat Stres, dan Tingkat kecemasan mahasiswa. Hasil penelitian menunjukkan bahwa Algoritma KNN dapat digunakan untuk mengklasifikasikan mahasiswa kedalam kategori tingkat stress tertentu dengan akurasi sebesar 83,33%, dengan data uji sebanyak 6 mahasiswa dan data training sebanyak 84 mahasiswa. Mahasiswa yang tergolong stres berat akan dilakukan penanganan secara intensif agar dapat dipulihkan kembali dengan cara melakukan pendekatan berkala dengan pendampingan seorang Psikolog yang ada di UNUGHA Cilacap. Selain itu, temuan ini menyoroti pentingnya teknologi kecerdasan buatan, khususnya Algoritma KNN, dalam mebantu mengidentifikasi faktor-faktor yang berkontribusi terhadap kesejahteraan psikologis mahasiswa, juga menekankan dampak stres pada perilaku dan kesejahteraan fisik mahasiswa termasuk kemungkinan munculnya emosi negative, kesulitan tidur, depresi, dan gangguan fisik lainnya. Temuan ini penting untuk pengembangan strategi intervensi yang lebih efektif dalam mendukung mahasiswa di lingkungan akademis yang penuh dengan tekanan. Penelititan ini menunjukan bahwa Aloritma KNN dapat digunakan sebagai alat prediksi yang efektif untuk memahami dan mengelola tingkat stres mahasiswa.
Analisis Perbandingan Fuzzy Tsukamoto dan Sugeno dalam Menentukan Jumlah Produksi Kain Tenun Menggunakan Base Rule Decision Tree Tundo, Tundo; Akbar, Riolandi; Sela, Enny Itje
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 1: Februari 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020701751

Abstract

Penelitian ini menerangkan tentang analisis perbandingan fuzzy Tsukamoto dan Sugeno dalam menentukan jumlah produksi kain tenun dengan menggunakan base rule decision tree. Dari hasil analisis penelitian ini, maka ditemukan beberapa perbedaan yang sangat signifikan: (1) Metode fuzzy Tsukamoto dari hasil yang diperoleh lebih mendekati dari data sesungguhnya, dibandingkan dengan fuzzy Sugeno, (2) Selisih yang diperoleh dengan menggunakan fuzzy Tsukamoto dengan data produksi sesungguhnya selalu konsisten yaitu hasil fuzzy Tsukamoto selalu lebih besar, sedangkan untuk fuzzy Sugeno tidak konsisten, (3) Hasil selisih untuk fuzzy Tsukamoto relatif mendekati dari data produksi sesungguhnya, sedangkan untuk fuzzy Sugeno relatif jauh selisih yang dihasilkan. Sehingga dapat disimpulkan bahwa metode yang paling mendekati nilai kebenaran adalah produksi yang mengunakan metode Tsukamoto dengan keakuratan yang diperoleh menggunakan base rule decision tree sebesar 83.3333 %.AbstractThis study describes the comparative analysis of fuzzy Tsukamoto and Sugeno determining the amount of woven fabric production using a decision tree base rule. From the results the analysis of this study, we found several very significant differences: (1) The fuzzy Tsukamoto method of the results obtained is closer to the actual, compared to fuzzy Sugeno, (2) The difference obtained by using fuzzy Tsukamoto with actual production data is always consistent is that Tsukamoto fuzzy results are always greater, while for Sugeno's fuzzy inconsistency, (3) The difference results for fuzzy Tsukamoto are relatively close to the actual production data, whereas Sugeno fuzzy is relatively far from the difference produced. So it can be concluded that the method closest to the truth value is production using the Tsukamoto method with the accuracy obtained using the base rule decision tree of 83.3333%.
Co-Authors Abdus Salam, Abdus Agung Yuliyanto Nugroho Ahmad Satria Rizqi Maula Akbar, Rasyan Akbar, Riolandi Akbar, Yuma Alief Prima Gani Amelia, Ika Anisah Nurul Azhar Arinal, Veri Arvianto, Ramdani Aryanti, Putri Gea Atsilah Daini Putri Aula, Raisah Fajri Aulia Nur Septiani Azhar, Anisah Nurul Betty Yel, Mesra Betty Yel, Mesra Bobby Arvian James Dadang Iskandar Mulyana` Dalail Dalail Dalail, Dalail Devia, Elmi Dewantara, Rizki Dewanti, Elsa Mayorita Dharmawan, Tio Dita Tri Yuliantoro Doni Kurniawan Doni Kurniawan Eldina, Ratih Enny Itje Sela Fadillah Abi Prayogo Fakhrurrofi Fakhrurrofi Fakhrurrofi, Fakhrurrofi Faldo Satria Faridatun Nisa Farras Abiyyu Handoko Fauzan Ibnu Sarky Galih Satria Yacob Gatra, Rahmadhan Hadi Gunawan, Hadi Haryati Heri Mahyuzar Heri Mahyuzar Humam Mu'asyir Husain Rahmani James, Bobby James, Bobby Arvian Januarsyah, Firly Joko Sutopo Junaidi Junaidi Kasiono, Roy Kastum Kastum Kastum Kastum, Kastum Kevin Arya Josaphat Sitompul Khafid Nurohman Khana, Rajes Kiki Setiawan Laily Nurmayanti Laras Sitoayu Lutfi Nugrahaini M. A. Burhanuddin Maharani, Delia Maharani, Shinta Aulia Mahardika, Fajar Mahyuzar, Heri Marcia Rizky Hamdala Marliani, Tiara Marthy, Nicola Mohd Khanapi Abd Ghani Mubarak, Zulfikar Yusya Muhammad Derry Oktaviandi Muhammad Nurdin Muhammad Nurdin Muhammad Raffiudin Muhammad Syazidan Nabilah, Laila Nandang Sutisna Nandang Sutisna Nisa, Faridatun Nizar, Amin Nugraha, Pramudya Nugrahaini, Lutfi Nugroho, Agung Yuliyanto Nugroho, Wisnu Dwi Nuradi, Fahmi Nurohman, Khafid Opi Irawansah, Opi Paidi, Imam Pramudya Nugraha Prayogo, Fadillah Abi Priyanto, Imansyah Purwasih, Intan Putri Wibowo, Salsabila Qolbi, Rofika Rachmat Hidayat Insani Rachmat Hidayat Insani Rachmawati, Dea Noer Raden Dewa Saktia Purnama Raffiudin, Muhammad Raihanah, Syifa Raisah Fajri Aula Ramadhan, Abhirama Huga Ramadhani, Devika Azahra Rasiban Rasiban Ridho Akbar Rindy Julianda Riolandi Akbar Rizki Maulana, Rizki Rohmat Wijaya Romadan, Diva Putra Rona Guines Purnasiwi Saidah, Andi Saifullah, Shoffan Saktia Purnama, Raden Dewa Sarimole, Frencis Matheos Setiawan, Kiki Shindy Apriani Shofwatul ‘Uyun Sodik Sopan Adrianto SOPAN ADRIANTO SRI LESTARI Sugeng Sugiono Sugiono Sugiyono Sugiyono Sugiyono Sugiyono Suropati, Untung Sutisna, Nandang Syani, Muhammad Syifa Raihanah Tampubolon, Parlindungan Tasti, Andi Thalita Tiara Ratu Alifia Tresia, Eflin Tri Wahyudi Tri Wahyudi Tundo Tundo Untung Suropati Untung Suropati Wafiqi, Achmad Ulul Azmi Wagiman, Wagiman Waloeya, Farhan Adriansyah Wijonarko, Panji Wisnu Dwi Nugroho Yacob, Galih Satria Yudisman Ferdian Bili