Article Per Year (5 Year)
p-Index From 2020 - 2025
2.391
P-Index
This Author published in this journals
All Journal JSiI (Jurnal Sistem Informasi) Jurnal Sains Indonesia Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Abdi Jurnal Publikasi Data : Journal of Information Systems and Management Digitus : Journal of Computer Science Applications Jurnal Ilmu Komputer
Tukiyat
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Mathematics Social Sciences Other

Published : 16 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 16 Documents
Search

 1   2 
ANALISIS PERBANDINGAN METODE TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION, SIMPLE ADDITIVE WEIGHTING DAN WEIGHTED PRODUCT DALAM SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN GURU TERBAIK Kanim; Tukiyat; Murni Handayani
Jurnal Sistem Informasi Vol 10 No 1 (2023)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v10i1.6134

Abstract

Tenaga Pendidik adalah sumber yang sangat penting bagi setiap sekolah dalam melangsungkan pendidikan anak bangsa, guru juga mempunyai tantangan sendiri bagi pihak pengelola lembaga pendidikan untuk dapat memberikan suatu keputusan yang terbaik, serta berkualiatas, guna membantu meningkatkan kualitas pendidikan dimasa yang akan datang. Namun SMPIT Rahmatutthoyyibah Al-Iflahah Kab. Tangerang, Pemilihan guru terbaiknya masih bersifat subjektif, sehingga dibutuhkan sistem pendukung keputusan untuk menentukan pemilihan guru yang terbaik yang ada di SMPIT Rahmatutthoyyibah Al-Iflahah Kab. Tangerang, dan untuk menentukan guru terbaik penulis menggunakan Metode Metode Technique for Order by Similarity to Ideal Solution (TOPSIS), Simple Additive Weighting (SAW), dan Weighted Product (WP). Pemilihan Guru terbaik dinilai dari 18 responden yakni Kepala Sekolah, 6 Staf Tenaga Pendidik , 10 murid kelas 9 dan 1 perwakilan dari wali murid. Kriteria dalam pemilihan guru terbaik adalah menguasai belajar mengajar, penilaian dan evaluasi, mengenal karakteristik peserta didik, pengembangan kurikulum, etos kerja dan tanggung jawab, kedisiplinan, hubungan guru dengan teman sejawat, bersikap inklusif, objektif, serta tidak diskriminatif, hubungan guru dengan wali murid / komite sekolah, dan yang terakhir yaitu kerja sama tim. Hasil dari implementasi dari ketiga metode ini pada semua kriteria dan sub kriteria dari 5 orang guru yang dinilai, sehingga Ibu Yulia, S.Pd. yang mendapatkan peringkat terbaik dengan nilai 0,707 (Metode TOPSIS), 0,705 (Metode SAW), dan 0, 231 (Metode WP). Dan untuk hasil proses perbandingan antara metode TOPSIS, SAW, dan WP bahwa WP adalah metode yang paling sesuai dengan prosentase 99,998% daripada metode TOPSIS dan SAW. Kata kunci: SPK, Guru Terbaik, TOPSIS, SAW, WP
ANALISIS PERKIRAAN KEBUTUHAN BAHAN BAKU EKSTRAK COFEE COLD BREW DI PT. MORADI Ruspendi; Nurmutia, Syahreen; Tukiyat; Misbah
Jurnal Sains Indonesia Vol. 3 No. 3 (2022): Volume 3, Nomor 3, 2022 (November)
Publisher : PUSAT SAINS INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59897/jsi.v3i3.97

Abstract

During the 2021 period, the total production of PT. Moradi often has advantages and disadvantages compared to the actual number of requests due to the availability of raw materials that are not suitable. Availability of raw materials in the right amount will support the smooth production process, so it is necessary to do proper forecasting. The forecasting process carried out in this study uses the methods of Moving Average, weighted Moving Average and exponential smoothing. From the calculation results, it is obtained that the estimated number of production needs for the next period using the Moving Average method is 220, the weighted Moving Average method is 218 and the exponential smoothing method is 212. Next is to compare the forecast error values ​​to determine one method to be used as basis for decision making. From the calculation of the error value, the most appropriate forecasting method is the exponential smoothing method = 0.3, with a Mean Absolute Deviation (MAD) value of 21.02, a Mean Square Error (MSE) of 656.95 and a Mean Absolute Percent Error (MAPE) value of 10%. , with the value distribution of the Tracking Signal control map, nothing crosses the upper limit of the UCL and the lower limit of the LCL.
PELATIHAN MEMBUAT KARYA TULIS ILMIAH DENGAN MEMANFAATKAN ARTIFISIAL INTELIGENCE (AI) BAGI PELAJAR SMK BINUSA KOTA TANGERANG SELATAN Tukiyat; Sajarwo Anggai; Arya Adhyaksa Waskita; Rafi Mahmud Zain; Muhammad Rafif Misbahuddin
Abdi Jurnal Publikasi Vol. 3 No. 2 (2024): November
Publisher : Abdi Jurnal Publikasi

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

Abstract

Scientific writing is an essential skill that students, particularly those in Vocational High Schools (SMK), need to master. However, many students face challenges in understanding research methodology and technical skills, often resulting in scientific works that do not meet academic standards. This study aims to enhance students' scientific writing skills through training based on Artificial Intelligence (AI) technology. This Community Service Program (PKM) was conducted at SMK Binusa, South Tangerang City, involving 22 participants from the Computer Network Engineering department. The implementation method consisted of theory sessions, practical sessions, and workshop discussions. Evaluation was carried out using pre-tests and post-tests to measure participants' understanding improvements. The training results revealed that most participants showed enhanced scientific writing abilities, with 43% rated as excellent and 31% as good. Additionally, the training materials and instructor quality were positively evaluated, with 57% of participants rating the materials as excellent and 60% rating the instructors as excellent. This training provided significant benefits, particularly in fostering innovation and participants' digital literacy skills. Nevertheless, several areas for improvement were identified, such as delivering more interactive materials and diversifying training methods. This program recommends integrating interactive technology and continuous training to support the development of scientific writing skills for vocational students, thereby enhancing their competitiveness in the future.
SOSIALISASI PENERAPAN TEKNOLOGI MODIFIKASI CUACA UNTUK PENGENDALIAN BENCANA BANJIR Tukiyat; Makhsun; Murni Handayani; Hesti Rahayuningsih; Hartanto
Abdi Jurnal Publikasi Vol. 3 No. 6 (2025): Juni
Publisher : Abdi Jurnal Publikasi

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

Abstract

The increasing intensity of climate change has triggered a rise in the frequency and impact of hydrometeorological disasters in Indonesia, such as floods, droughts, and forest fires. This condition demands the strengthening of community capacity in understanding and anticipating disaster risks. This Community Service (PKM) activity aims to educate the people of Ciomas District, Bogor Regency, about hydrometeorological disaster mitigation and the introduction of Weather Modification Technology (WMT) as an adaptive solution. The implementation methods included seminars, counseling sessions, interactive discussions, and evaluations through pre-tests and post-tests. The results show that the majority of participants came from civil servants and village apparatus (48%), with 83% stating that the material was easy to understand. A significant increase in understanding was recorded from pre-test to post-test, indicating effective material delivery. Additionally, participants expressed high enthusiasm for utilizing technology in disaster management, particularly WMT. These findings reinforce the importance of continuous dissemination of disaster information through information technology to the public. This activity contributes positively to building community preparedness in facing climate change and supports collaboration between academia, government, and the public in scientific-based disaster mitigation strategies. In the future, further assistance is needed to encourage the local implementation of adaptive technologies.
Analisis Prediksi Curah Hujan di Kota Tangerang Menggunakan Metode LSTM dan GRU Supriatna, Dahlan; Anggai, Sajarwo; Tukiyat
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1068

Abstract

Curah hujan yang tidak menentu dapat memengaruhi berbagai sektor, seperti pertanian, energi, dan infrastruktur. Akurasi prediksi curah hujan sangat penting untuk mitigasi risiko bencana banjir maupun kekeringan. Penelitian ini bertujuan untuk membandingkan akurasi prediksi curah hujan menggunakan dua algoritma deep learning, yaitu LSTM dan GRU serta dapat memberikan kontribusi pada pengelolaan sumber daya air yang lebih efektif. Model ini diterapkan pada data historis curah hujan dan variabel meteorologi terkait, data penelitian adalah data sekunder yang bersumber dari data BMKG Kota Tangerang periode Januari 2014 – Januari 2025 sebanyak 4.062 data. Evaluasi kinerja model dilakukan menggunakan metrik seperti MAE, MSE, RMSE, dan R². Hasil menunjukan Model LSTM dengan konfigurasi hyperparameter optimal—terdiri dari timesteps 36 bulan, 64 unit memori, 100 epoch pelatihan, batch size 16, dropout 0.3, dan learning rate 0.0001—menghasilkan metrik evaluasi terbaik MAE sebesar 0.08473, MSE sebesar 0.00973, RMSE sebesar 0.09863, dan R2 sebesar 0.65601. Nilai R2 yang relatif tinggi ini mengindikasikan bahwa model LSTM mampu menjelaskan sekitar 65.6% dari variabilitas dalam data curah hujan aktual. Sebagai perbandingan, model GRU dengan kinerja terbaiknya (menggunakan batch size 32) menunjukkan metrik evaluasi yang sedikit di bawah LSTM, yaitu MAE 0.08883, MSE 0.01078, RMSE 0.10383, dan R2 Score 0.61878, secara keseluruhan, LSTM terbukti lebih unggul dalam kapabilitas prediksinya.
Phishing Email Classification Approach Using Machine Learning Algorithms - A Literature Review Firman; Tukiyat; Wiharjo, Sudarno
Data : Journal of Information Systems and Management Vol. 3 No. 3 (2025): July 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/data.v3i3.692

Abstract

Email phishing is one of the cybersecurity threats that continues to grow, utilizing social engineering to obtain sensitive data. Various machine learning-based approaches have been researched to detect and classify phishing emails. This article presents a literature review of phishing email classification methods, including the K-Nearest Neighbor (KNN) algorithm, Naïve Bayes, Support Vector Machine (SVM), Random Forest, and deep learning-based approaches. The discussion included feature extraction techniques (TF-IDF, Word2Vec, BERT), handling data imbalances, and model performance evaluation. This review identifies current research trends, challenges, and gaps for further research.
Analysis of Broiler Chicken Production Success Classification Using K-Nearest Neighbors And Naive Bayes Methods at PT. Jandela Jaga Kaloka (Jajaka) Tukiyat; Anggai, Sajarwo; Agnia Bilqisti
Digitus : Journal of Computer Science Applications Vol. 2 No. 4 (2024): October 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v2i4.396

Abstract

The livestock subsector, particularly broiler chickens, provides animal protein sources in Indonesia. However, low production efficiency, managerial challenges, and productivity fluctuations remain the primary obstacles to achieving sustainability in this sector. This study aims to analyze the success rate of broiler chicken production at PT. Jandela Jaga Kaloka (JAJAKA) using a data mining classification approach with the K-Nearest Neighbors (K-NN) and Naive Bayes algorithms. The research population comprises broiler production data from various branches of PT. JAJAKA, with a sample of 200 datasets selected based on representative criteria. The study employs the hold-out method with data splits of 60:40 and 70:30 for training and testing the models. The success rate of production is classified into three categories: good, less good, and excellent. The findings reveal that the K-NN algorithm outperforms with an accuracy of 92.59%, compared to Naive Bayes, which achieves 76.67%. Regarding recall, K-NN records a value of 96.67%, higher than Naive Bayes at 71.67%. However, Naive Bayes shows slightly better precision (94.29%) than K-NN (93.55%). These results affirm that the K-NN algorithm is more effective for classifying the success rate of broiler chicken production, supporting PT. JAJAKA in making more precise and strategic managerial decisions. Furthermore, this study contributes significantly to developing data mining methods in the poultry farming sector to improve efficiency and productivity sustainably. It provides valuable insights for PT. Jandela Jaga Kaloka will evaluate the success rate of broiler chicken production, facilitating more accurate managerial decision-making.
Analisis Aplikasi Iuran Pengelolaan Lingkungan Berbasis Web Dengan Proses Monitoring dan Evaluasi COBIT 4.1 (Studi Kasus Perumahan Metro Residence) Edlianto, Dionisius Riyan; Susanto, Agung Budi; Tukiyat
Jurnal Ilmu Komputer Vol 2 No 1 (2024): Jurnal Ilmu Komputer (Edisi Juli 2024)
Publisher : Universitas Pamulang

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

Abstract

Metro Residence Housing has a total of 381 houses. The sales admin manually inputs environmental management contribution data into Excel every month. To address this, an application was created using the Scrum method, chosen for its adaptability and efficiency. Sprints one to three, each lasting two weeks, completed in six weeks. Post-development, a black box test with eight menu tests confirmed the system's functionality, with all tests passed. Subsequent direct testing with the admin over a month led to application acceptance. An IT governance audit followed, involving five respondents: sales admin, estate admin, admin manager, branch IT staff, and HO IT staff. The COBIT 4.1 audit rated Bogor Metro Residence at level three, indicating standardized, documented, and communicated IT procedures. Limited IT staff understanding prevented a higher score. The application can be further developed into a mobile app for residents to monitor bills and make payments.
Ekstraksi Topik dalam Dataset Menggunakan Teknik Pemodelan Topik Anggai, Sajarwo; Tukiyat; Rivai, Abu Khalid; Zain, Rafi Mahmud
Jurnal Ilmu Komputer Vol 2 No 1 (2024): Jurnal Ilmu Komputer (Edisi Juli 2024)
Publisher : Universitas Pamulang

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

Abstract

The issue in this research is the lack of understanding regarding the main topics and their changes in speeches and media publications related to President Joko Widodo. This study aims to identify, analyze, and predict changes in key topics within speeches, statements, and media publications related to President Joko Widodo using Latent Dirichlet Allocation (LDA) topic modeling techniques. The research employs a quantitative approach to analyze President Joko Widodo's speech texts using the Latent Dirichlet Allocation (LDA) method. The process began with scraping documents from the official website of the Republic of Indonesia's Secretariat, resulting in 5,988 speech transcripts from October 20, 2014, to March 2, 2024. Text preprocessing involved tokenization, stopword removal, and stemming/ lemmatization, followed by dictionary-term formation. The findings indicate that the model with k=16 has the highest coherence (0.554) and the best perplexity at k=21 (-13.130). The main topics identified include Nationalism and National Values, Regional Government, and Education and Children. Topic visualization with PyLDAvis aids in the exploration and identification of topics, providing insights for decision-making and policy development. To enhance understanding of topic changes, it is recommended to conduct trend analysis on key topics over time. This will help identify how President Joko Widodo's priorities shift and respond to new issues. By monitoring these trends, the research can provide deeper insights into the evolution of policies and the President's focus.
Analisis Disaster Recovery Plan Keamanan Data dan Informasi Menggunakan NIST Framework (Studi Kasus: Biro Teknologi Informasi Yayasan Pendidikan Internal Audit) Muhamad, Faruk; Tukiyat; Anggai, Sajarwo
Jurnal Ilmu Komputer Vol 2 No 1 (2024): Jurnal Ilmu Komputer (Edisi Juli 2024)
Publisher : Universitas Pamulang

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

Abstract

Disasters are unexpected and potentially significant risks to the continuity of company and organization operations, especially those related to information systems and information technology (IS/IT). The Internal Audit Education Foundation (YPIA) in handling disasters related to data and information security often faces obstacles that cause problems that become more widespread in the future. Therefore, a disaster recovery plan (DRP) becomes an urgent need. The purpose of this study is to evaluate resilience to disasters and data and information security attacks, and to ensure better business continuity in the face of emergency situations. Researchers use the National Institute of Standards and Technology (NIST) Framework in conducting a DRP analysis of security and data. The study begins by identifying and evaluating risks, conducting risk assessments, conducting Business Impact Analysis (BIA) determining preventive controls, and formulating contingency strategies. This study produces priority handling of high maturity risks in data damage, with an initial risk value of 3.8 and an impact of 4.4. After the control was carried out, there was a residual risk with a risk value of 1.6 and an impact of 3, with a very low maturity level and a residual value of 13.5 (80%). The reduction in the risk of data damage was significant with a very low residual value, indicating that the implementation of DRP using the NIST Framework in risk mitigation on critical assets of the Internal Audit Education Foundation was quite effective.
Co-Authors Abu Khalid Rivai Achmad Hindasyah Ade Sekarningsih Agnia Bilqisti Agung Budi Susanto Andriana Dwi Hastanto Andrie Febriansyah Anggai, Sajarwo Any Widyawati Arya Adhyaksa Waskita Ceppy Multi Anggara Citra Kusmardani Cut Shifa Khoirunnisa Dian Herdianingsih Dwi Rachmadi Dwi Yuwono Edlianto, Dionisius Riyan Eko Widyantoro HS Erian Tasa Fahmi Khoirul Ichsan Famiyana Dewi Farid Faisal Firman Hanapi Hartanto Hesti Rahayuningsih Kanim Makhsun Misbah Muhamad Farid Hasan Khadafi Muhamad, Faruk Muhammad Rafif Misbahuddin Murni Handayani Nurdin Wibowo Nurmutia, Syahreen PUJI LESTARI Rafi Mahmud Zain Ruspendi Sarnubih Hasan Shendyko Wicaksono Supriatna, Dahlan Tri Nurmayati Tri Suwito Abdhy Warno Mulud Wibowo, Rizki Septiyanto Wiharjo, Sudarno Winarni Zain, Rafi Mahmud