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Temu Kembali Informasi Big Data Menggunakan K-Means Clustering Imam Marzuki
SMATIKA JURNAL Vol 5 No 02 (2015): Smatika Jurnal : STIKI Informatika Jurnal
Publisher : LPPM STIKI MALANG

Saat ini manusia hidup selalu berhubungan dengan data, dimana data dibuat dan dikirimkan tiap detiknya di seluruh dunia. Hal ini menyebabkan data di jaringan bertambah secara massive (besarbesaran).Oleh karena itu kebutuhan akan pengelolaan data tersebut semakin meningkat. Salah satu bagian yang penting dalam pengelolaan data adalah proses pencarian informasi yang diinginkan oleh pengguna atau biasa disebut dengan temu kembali informasi (information retrieval). Tujuan utama dari temu kembali informasi adalah menemukan kembali dokumen yang berisi informasi yang relevan dengan query yang di inputkan oleh pengguna. Sudah banyak metode yang diusulkan untuk temu kembali informasi. Namun dari sekian teknik masih menyisakan permasalahan terkait kecepatan dan akurasi pencarian. Pada tesis ini, penulis mengusulkan metode terbaik temu kembali informasi pada pencarian big data. Permasalahan muncul ketika melakukan proses pencarian informasi. Hal ini disebabkan big data didominasi oleh data tidak terstruktur. Data tidak terstruktur memiliki sifat sulit diorganisir. Oleh karena itu, diperlukan suatu teknik khusus untuk mengatasinya. Salah satu solusi untuk mengatasi permasalahan tersebut adalah dengan menambahkan klasterisasi dalam indexing informasi. Dalam penelitian ini digunakan metode klasterisasi menggunakan kmeans clustering. Berdasarkan hasil percobaan menggunakan k-means clustering didapatkan nilai rata-rata precision 0.8 nilai rata-rata recall 0.741, dan nilai rata-rata waktu komputasi 0.579 detik.

PENGEMBANGAN LKM BERBASIS MC-GP MENGGUNAKAN CAT UNTUK MELATIHKAN KEMAMPUAN BERPIKIR KRITIS MAHASISWA Indro Wicaksono; Sutarto Sutarto; Imam Marzuki
KONSTAN - JURNAL FISIKA DAN PENDIDIKAN FISIKA Vol 4 No 1 (2019): KONSTAN (Jurnal Fisika dan Pendidikan Fisika)
Publisher : Universitas Islam Negeri (UIN) Mataram

Tujuan penelitian ini adalah menghasilkan perangkat pembelajaran berupa Lembar Kegiatan Mahasiswa (LKM) berbasis Media Cetak Gambar Proses (MC-GP) menggunakan Computer And Thecnology (CAT) pada matakuliah Fisika Dasar materi kinematika gerak lurus yang layak digunakan untuk melatihkan kemampuan berpikir kritis mahasiswa. Subjek penelitian ini yaitu mahasiswa semester 1 prodi Teknik Industri dan Teknik Elektro Universitas Panca Marga (UPM) Tahun Ajaran 2018/2019. Kemampuan berpikir kritis yang diukur menyesuaikan dengan indikator berpikir kritis yang disingkat dengan FRISCO. Model pengembangan yang digunakan merujuk pada model pengembangan 4-D dengan tahapan Definition, Design, Development, dan Dissemination. Desain penelitian yang digunakan yaitu One group pretest-postest design. Teknik pengumpulan data penelitian menggunakan instrumen penelitian yang meliputi instrumen validasi LKM dari ahli media, instrumen tes kemampuan berpikir kritis (TKBK) berupa tes, dan instrumen respon mahasiswa berupa angket. Hasil penelitian yang dihasilkan menunjukkan bahwa hasil validasi LKM MC-GP dan TKBK berkategori valid. Hasil TKBK mahasiswa mengalami peningkatan dengan skor rata-rata peningkatannya 0,73 (gain tinggi). Respon mahasiswa terhadap penggunakan LKM MC-GP berkategori sangat kuat.

Temu Kembali Informasi Big Data Menggunakan K-Means Clustering Imam Marzuki
JURNAL INTEGRASI Vol 7 No 2 (2015): Jurnal Integrasi - Oktober 2015
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar

This day, human life is always associated with the data, where the data is created and sent each second worldwide. This causes the data in the network increases massive (massive). Hence the need for data management is increasing. One important part of data management is the process of finding information desired by a user or commonly referred to as information retrieval (information retrieval). The main purpose of information retrieval is to rediscover documents containing information relevant to the query that is fed by the user. There have been many proposed methods for information retrieval. But of the technique still has problems related to the speed and accuracy of searches. In this thesis, the authors propose the best methods of information retrieval in search of big data. Problems arise when the process of seeking information. This is due to big data is dominated by unstructured data. Unstructured data have properties difficult to organize. Therefore, we need a special technique to ov ercome. One solution to overcome these problems is to add clustering in indexing information. This study used a clustering method using k-means clustering. Based on the experimental results using k-means clustering obtained average value of 0.8 precision recall the average value of 0.741, and the average value of 0579 seconds of computing time.

Rekonfigurasi Jaringan Radial Distribusi Tenaga Listrik Penyulang Suryagraha Menggunakan Binary Particle Swarm Optimization (BPSO) Diana Mulya Dewi; Nuzul Hikmah; Imam Marzuki; Ahmad Izzuddin
Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan Vol. 9 No. 2 (2018): Oktober 2018
Publisher : FT- UNDAR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48056/jintake.v9i2.42

A radial distribution electrical network at a certain distance will have a large voltage loss due to conductive losses, especially at the end point. The tip voltage is determined by the distance of the distribution and the amount of load. The form of configuration also affects the amount of power loss and voltage loss. So that a good configuration is needed in order to obtain good efficiency. Reconfiguration of the distribution network is used to reset the network configuration form by opening and closing switches on the distribution network. Reconfiguration is expected to reduce power losses and improve distribution system reliability. Many feeders and buses on the network if calculated manually will be difficult and require a very long time. So it is necessary to solve problems using program assistance. In this case, use Particle Swarm Optimization (PSO). Particle Swarm Optimization (PSO) algorithm based on the behavior of a herd of insects, such as ants, termites, bees or birds. BPSO is a development of the PSO algorithm designed to solve the optimization problem in a discrete combination, where the particle takes the value of binary vectors with length n and speed which is defined as the probability of xn bits to reach value 1. The results show a significant reduction in losses .

Perancangan dan Pembuatan Sistem Penyalaan Lampu Otomatis Dalam Ruangan Berbasis Arduino Menggunakan Sensor Gerak dan Sensor Cahaya Imam Marzuki
Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan Vol. 10 No. 1 (2019): Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan
Publisher : FT- UNDAR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48056/jintake.v10i1.48

The manual use of household lights poses a problem in the efficiency of electricity use. Humans with various activities often do not pay attention to the condition of the lights in the room whether it lights up or not. This is certainly a waste of electricity when they leave the room without turning off the switch. Therefore, researchers propose an automation system that can save electricity from lighting. In designing the system there are two sensors used, namely Passive Infra Red (PIR) sensor and Light Dependent Resistror (LDR) sensor. PIR functions to detect the presence of human movements (objects) in the sensor work area, while the LDR sensor functions to detect the intensity of light around the room. The results of the system testing are that the PIR sensor works well around ± 2 minutes after the device is activated and the LDR Sensor will continuously measure the value of light intensity then turn on and off the light according to the programmed

Analisis Kelayakan Pengoperasian Instalasi Pengolahan Air Limbah (IPAL) CV Proma Tun Probolinggo Tri Prihatiningsih; Haryono; Imam Marzuki
Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan Vol. 10 No. 1 (2019): Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan
Publisher : FT- UNDAR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48056/jintake.v10i1.49

Tofu is a traditional Indonesian food that is very popular with the community. The tofu production process produces solid and liquid waste. The liquid waste contains very high BOD, COD and TSS which has the potential to pollute the environment, therefore it is necessary to have a Waste Water Treatment Plant (WWTP). This study aims to determine the economic feasibility study of the operation of the WWTP. The research was conducted at CV. Proma Tun Saroyyan Probolinggo. To find out the economic feasibility of the operation of WWTPs using the parameters of the Net Present Value (NPV), Payback Period (PP) and Benefit Cost Ratio (B / C R) The results showed that the WWTP used was the Anaerob-Biogas system, the anaerobic system is wastewater treatment by utilizing microorganisms that work without oxygen free, with three stages, namely: Hydrolysis, Acidification, and Methane Formation Stage. The resulting methane gas (biogas) is a mixture of various types of gases, including CH4 (54% -70%), CO2 (27% -45%), O2 (1% -4%), N2 (0.5% -3%), CO (1%), and H2 which are stored in the gas holder and distributed to the community as an alternative fuel to replace LPG gas. For an analysis of economic feasibility with NPV criteria shows that the biogas WWTP is profitable, because the NPV value is positive at Rp. 58,249,000. Based on the criteria of PP, which is 5 years 10 months 20 days, which means that the time is quite short and does not exceed the age of the economical gas holder. And the value of B / C Ratio is 1.4109 which means that the WWTP biogas in the CV. Proma Tun Saroyyan Probolinggo deserves to be developed because of the value of B / C R> 1.

PERHITUNGAN MATEMATIS KLASTERISASI NILAI MATA KULIAH MAHASISWA MENGGUNAKAN ALGORITMA K-MEANS Nurhidayati; Imam Marzuki
AL JABAR: Jurnal Pendidikan dan Pembelajaran Matematika Vol. 2 No. 2 (2023): Juli
Publisher : LPPM STAI Muhammadiyah Probolinggo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46773/aljabar.v2i2.778

This research aims to conduct mathematical calculations related to the clustering of statistical course grades among students. The author utilized data from several students enrolled in the Mathematics Education Study Program at STAI Muhammadiyah Probolinggo, who took the statistics course as a case study. The clustering algorithm employed in this study is K-Means. The analysis results provide significant insights into the students' abilities in the statistics course. This research offers potential benefits to the study program in curriculum development and the design of more effective teaching strategies. The findings can be used to identify student groups in need of additional attention and to comprehend academic behavior patterns that may affect learning outcomes. In this study, only grade atributes such as assignments, mid-term, and final exam scores were utilized. Nevertheless, it is worth noting that the K-Means algorithm can be applied to cluster data with multiple grade atributes, including attendance, discipline, participation, and other atributes relevant to student learning.Keywords : mathematical calculations, identification, clustering, attributes

Temu Kembali Informasi Big Data Menggunakan K-Means Clustering Imam Marzuki
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 5 No 02 (2015): Smatika Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v5i02.75

Saat ini manusia hidup selalu berhubungan dengan data, dimana data dibuat dan dikirimkan tiap detiknya di seluruh dunia. Hal ini menyebabkan data di jaringan bertambah secara massive (besarbesaran).Oleh karena itu kebutuhan akan pengelolaan data tersebut semakin meningkat. Salah satu bagian yang penting dalam pengelolaan data adalah proses pencarian informasi yang diinginkan oleh pengguna atau biasa disebut dengan temu kembali informasi (information retrieval). Tujuan utama dari temu kembali informasi adalah menemukan kembali dokumen yang berisi informasi yang relevan dengan query yang di inputkan oleh pengguna. Sudah banyak metode yang diusulkan untuk temu kembali informasi. Namun dari sekian teknik masih menyisakan permasalahan terkait kecepatan dan akurasi pencarian. Pada tesis ini, penulis mengusulkan metode terbaik temu kembali informasi pada pencarian big data. Permasalahan muncul ketika melakukan proses pencarian informasi. Hal ini disebabkan big data didominasi oleh data tidak terstruktur. Data tidak terstruktur memiliki sifat sulit diorganisir. Oleh karena itu, diperlukan suatu teknik khusus untuk mengatasinya. Salah satu solusi untuk mengatasi permasalahan tersebut adalah dengan menambahkan klasterisasi dalam indexing informasi. Dalam penelitian ini digunakan metode klasterisasi menggunakan kmeans clustering. Berdasarkan hasil percobaan menggunakan k-means clustering didapatkan nilai rata-rata precision 0.8 nilai rata-rata recall 0.741, dan nilai rata-rata waktu komputasi 0.579 detik.

Improving Random Forest Performance for Botnet Attack Detection in IoT Big Data Using Remove Frequent Values Filter Imam Marzuki; Mas Ahmad Baihaqi; Hartawan Abdillah; Dwi Iryaning Handayani; Nurhidayati Nurhidayati
International Journal of Electrical and Intelligent Engineering Vol 1, No 1 (2025)
Publisher : Department of Electrical Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ijeie.v1i1.34533

This research aims to enhance the performance of the Random Forest algorithm in classifying big data within the Internet of Things (IoT) domain, specifically for detecting botnet attacks. The study utilizes the N-BaIoT dataset, comprising 150,000 instances of IoT network traffic categorized into normal and anomalous (botnet) data. To optimize classification outcomes, a preprocessing technique—the “remove frequent values” filter—is applied to reduce redundancy and improve computational efficiency. Model performance is evaluated using accuracy, precision, recall, and F1-score. Experimental results demonstrate that this filter improves classification accuracy from 99.976% to 99.998%, with precision, recall, and F1-score all reaching 1.000. Cross-validation was conducted to ensure the robustness of these results. These findings suggest that even lightweight preprocessing techniques can significantly enhance machine learning performance in IoT big data classification tasks.

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