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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Proceeding of the Electrical Engineering Computer Science and Informatics Indonesian Journal on Software Engineering (IJSE) Faktor Exacta INOVTEK Polbeng - Seri Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal ICT : Information Communication & Technology SENSITEK Respati SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Abdimas Sangkabira Kresna: Jurnal Riset dan Pengabdian Masyarakat Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) International Journal on Advanced Technology, Engineering, and Information System (IJATEIS) Eduvest - Journal of Universal Studies Telematika MKOM Jurnal Pengabdian Masyarakat Nasional Artinara Jurnal Ticom: Technology of Information and Communication INOVTEK Polbeng - Seri Informatika
Mohammad Syafrullah
Universitas Budi Luhur
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Neuroscience Social Sciences Other

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Penerapan Algoritma C4.5 Pada Imbalanced Dataset Untuk Memprediksi Kegagalan Angsuran Properti Yodi Susanto; Devit Setiono; Muhammad Syafrullah
Jurnal ICT : Information Communication & Technology Vol 20, No 2 (2021): JICT-IKMI, Desember 2021
Publisher : STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36054/jict-ikmi.v20i2.372

Abstract

In this research, the data collection carried out by studying the patterns of consumers who fail to pay, which aimed to build a model so that it could be used in predicting customers who have the potential to fail to pay. The research used the Cross-Industry Standard Process for Data Mining (CRISP-DM) method with details of the business understanding process, data understanding, data preparation, modeling, evaluation and deployment / interpretation. The dataset in this research was taken from sales, cancellation and consumer data from January 2016 to December 2019. Because the dataset in this research was an imbalanced dataset, the researchers tried to use Synthetic Minority Oversampling Technique (SMOTE) in handling the imbalanced dataset. The research conducted a comparison of the value of accuracy, precision, recall, f measure and Area Under the ROC Curve (AUC) between the original dataset and the dataset for the addition of the SMOTE technique to several algorithms including C4.5, K-NN and Naïve Bayes. The attributes used in this research were source of funds, purpose of purchase, age, selling price, occupation, total installments, percentage of total installments, monthly installments, percentage of late installments and status. From the comparison, it was found that the C4.5 algorithm with the SMOTE 480% dataset had the highest accuracy value of 97.62%, precision of 0.976, recall of 0.976, f measure of 0.976 and AUC of 0.986 which meant Excellent Classification. From the research conducted, it was expected that the model formed on the imbalanced dataset with the C4.5 and SMOTE algorithms could be used to predict consumer installment failures.
Pengembangan Knowledge Management System Untuk Mengelola Pengetahuan Personel Pada Laboratorium Pengujian Slid Seameo Biotrop Zulkarnaen Noor Syarif; Mohammad Syafrullah; Devit Setiono; Irawan Irawan; Hendri Irawan
Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Vol 19, No 1 (2022): APRIL 2022
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/bit.v19i1.1837

Abstract

Knowledge of SOPs, documents, regulations, and experience is not well documented in the SEAMEO BIOTROP SLID environment to support the process of disseminating knowledge (knowledge) between employees in the SEAMEO BIOTROP SLID environment. Personnel experience and knowledge are lost when personnel are replaced, transferred, retired, or have expired personnel. At present, knowledge and experience in daily work accumulates in each personnel and is not documented in documents and systems, so it depends on each personnel. This research uses a methodology developed by Fernandez and Sabherwal. The results of this study are at the forefront of developing the knowledge management process. The knowledge management process developed at SLID SEAMEO BIOTROP is externalization, internalization, outreach for knowledge sharing, direction, routine, combination, socialization for discovery and knowledge sharing. Features created by a knowledge management system to support the knowledge management process consist of document management, knowledge management, discussion forums, and search capabilities. The prototype knowledge management system has been tested by SLID SEAMEO BIOTROP personnel using the user acceptance testing method and overall results are included in the evaluation criteria very well at the level of 84.51%.
ALGORITMA NAÏVE BAYES DALAM ANALISIS SENTIMEN UNTUK KLASIFIKASI PADA LAYANAN INTERNET PT.XYZ Aria Mustofa Hidayat; Mohammad Syafrullah
Telematika MKOM Vol 9, No 2 (2017): Jurnal Telematika MKOM Vol. 9 No. 2 Juli 2017
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (426.863 KB)

Abstract

Kebiasaan masyarakat untuk mem-posting tweet yang memberikan informasi atau feedback terhadap suatu produk dapat dimanfaatkan sebagai landasan untuk mengetahui sentimen terhadap produk penyedia layanan internet yang ada di Indonesia. Kesulitan untuk mengolah data dan pengklasifikasian sentimen positif dan negatif yang tersedia pada media sosial Twitter serta nilai akurasinya menjadi suatu permasalahan. Pada penelitian ini akan membahas mengenai cara pengolahan data dari Twitter dengan tujuan untuk melakukan pengklasifikasian terhadap sentimen positif dan sentimen negatif dari posting tweet serta mencari akurasi dari metode yang digunakan yaitu Naïve Bayes Classifier. Data tweets yang digunakan yaitu sebanyak 500 data dimana masing-masing data positif sebanyak 250 data dan data negatif sebanyak 250 data. Hasil dari eksperimen yang dilakukan dalam penelitian ini menunjukkan bahwa nilai akurasi klasifikasi dari metode Naïve Bayes Classifier yaitu sebesar 91.00%.
KLASIFIKASI KERUSAKAN KAWASAN KONSERVASI DENGAN METODE SUPPORT VECTOR MACHINE (SVM) MENGGUNAKAN KERNEL GAUSSIAN: STUDI KASUS THE NATURE CONSERVANCY Syaiful Anwar; Mohammad Syafrullah
Telematika MKOM Vol 8, No 2 (2016): Jurnal Telematika MKOM Vol. 8 No. 2 September 2016
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (629.752 KB)

Abstract

Untuk dilakukan konservasi alam perlu dilakukan perancangan sebuah kawasan dengan analisa data dari hasil survey lalu diolah dan dipetakan pada aplikasi GIS dan dilakukan penentuan kawasan konservasi dengan melibatkan pakar setiap dilakukan perancangan. SVM merupakan metode analisis data yang digunakan untuk membentuk model yang mendeskripsikan kelas data yang penting, atau model yang memprediksikan trend data. Pada penelitian ini, untuk mengatasi kendala aplikasi metode SVM pada data yang tidak dapat dipisah secara linier digunakan metode kernel berbasis Gaussian. Metode kernel lain yaitu kernel polynomial juga akan dibahas untuk dibandingkan dengan metode kernel Gaussian. Hasil dari penelitian ini menunjukan bahwa penggunaan kernel Gaussian dapat meningkatkan tingkat akurasi dari klasifikasi data menggunakan SVM dengan pengujian dataset Pulau Sumba dengan menggunakan fungsi kernel Gaussian tingkat akurasi klasifikasi mencapai 98.3% dan menggunakan kernel Polynomial 73,09%.
Penerapan Algoritma C4.5 Pada Imbalanced Dataset Untuk Memprediksi Kegagalan Angsuran Properti Devit Setiono; Yodi Susanto; Mohammad Syafrullah
Jurnal ICT: Information Communication & Technology Vol. 21 No. 2 (2021): JICT-IKMI, Desember 2021
Publisher : LPPM STMIK IKMI Cirebon

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

Abstract

In this research, the data collection carried out by studying the patterns of consumers who fail to pay, which aimed to build a model so that it could be used in predicting customers who have the potential to fail to pay. The research used the Cross-Industry Standard Process for Data Mining (CRISP-DM) method with details of the business understanding process, data understanding, data preparation, modeling, evaluation and deployment / interpretation. The dataset in this research was taken from sales, cancellation and consumer data from January 2016 to December 2019. Because the dataset in this research was an imbalanced dataset, the researchers tried to use Synthetic Minority Oversampling Technique (SMOTE) in handling the imbalanced dataset. The research conducted a comparison of the value of accuracy, precision, recall, f measure and Area Under the ROC Curve (AUC) between the original dataset and the dataset for the addition of the SMOTE technique to several algorithms including C4.5, K-NN and Naïve Bayes. The attributes used in this research were source of funds, purpose of purchase, age, selling price, occupation, total installments, percentage of total installments, monthly installments, percentage of late installments and status. From the comparison, it was found that the C4.5 algorithm with the SMOTE 480% dataset had the highest accuracy value of 97.62%, precision of 0.976, recall of 0.976, f measure of 0.976 and AUC of 0.986 which meant Excellent Classification. From the research conducted, it was expected that the model formed on the imbalanced dataset with the C4.5 and SMOTE algorithms could be used to predict consumer installment failures.
Sentiment Analysis On The Reviews Of Civil Registration And Population Service Sub-District Of West Jakarta Using K-Nearest Neighbor Algorithm Emil Salim; Mohammad Syafrullah
Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Vol 20, No 1 (2023): APRIL 2023
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/bit.v20i1.2186

Abstract

Population administration plays an important role in Indonesia as it is involved in various activities. At the regional government level, the Population and Civil Registration Office is responsible for these matters. In West Jakarta, the Sub-district Population and Civil Registration Office is a government body that handles the technical aspects of population administration. Based on data from 2020, the population in West Jakarta reached 2,434,511 people. This office is responsible for providing services to the community, which involves various responses and opinions from the public. Some opinions are positive and constructive, while others may be dissatisfied with the services provided or have unmet expectations. This research aims to analyze public perspectives on the administration services of the Sub-district Population and Civil Registration Office in West Jakarta using machine learning approach, specifically the CountVectorizer feature extraction and K-Nearest Neighbor algorithm. The dataset used is sourced from an online form accessible by society. The analysis results from 386 reviews show that there are 311 (80.57%) positive reviews and 75 (19.43%) negative reviews, with the best testing performed using a K value of 3, achieving an accuracy rate of 83%, precision of 82%, and recall of 100%.
Comparison of Monthly Rainfall Prediction using Long Short Term Memory and Multi Layer Perceptron Methods in South Tangerang City Gaol, GA Monang Lumban; Syafrullah, Mohammad; Supardi, Supardi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2149

Abstract

Rainfall is one of the meteorological and climatological parameters whose information must be disseminated to the public and related stakeholders. Rainfall information has an important role in the sectors of people's lives. In agriculture, the amount of rainfall has an important role in determining the planting season, so that this can prevent potential crop failure. On Disaster, South Tangerang City during the 2016-2021 period experienced floods, landslides, and droughts. Therefore, the importance of rainfall prediction information can improve meteorological and climatological information services in various sectors. Nevertheless, it is still difficult for the community and stakeholders to get monthly rainfall predictions with high accuracy in the long term. In this research, monthly rainfall prediction is designed using MLP (Multi Layer Perceptron) and LSTM (Long Short Term Memory). The data used is the monthly rainfall data of Climate Hazards Group InfraRed Precipitations (CHIRPS) for 42 years (period 1981-2022) with coordinate boundaries according to the research location, namely South Tangerang City, which is located between 106.625 º - 106.825 º East and 6.4 ° - 6.2 ° LS as many as 16 grids with a resolution of 0.05 ° each grid. Monthly rainfall prediction using MLP produces an RMSE value of 90.19, and a MAPE of 40.55, while the LSTM method produces an RMSE value of 88.12 and a MAPE of 40.49. Monthly rainfall prediction results using the LSTM method are better than the MLP method; this can be seen from the RMSE value of the LSTM method is smaller than MLP.
PENGKLASTERAN DAN SEGMENTASI KARAKTERISTIK DONATUR SEDEKAH DARING DENGAN TEKNIK PENAMBANGAN DATA Martono, Martono; Syafrullah, Mohammad
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4223

Abstract

Dalam era kemajuan filantropi dan platform digital yang semakin berkembang, PPPA Daarul Qur’an aktif dalam mengumpulkan dana amal online, terutama zakat, sedekah, dan wakaf (ziswaf). Selain itu, mereka juga terlibat dalam program dakwah dan sosial, dengan fokus pada pembangunan masyarakat melalui tahfizhul Qur'an, beasiswa, bantuan kemanusiaan, dan kesehatan. Tantangan utama yang dihadapi adalah kesulitan dalam menentukan profil donatur potensial dan menyasar program ziswaf dengan tepat. Dalam penelitian ini, segmentasi data donatur dilakukan menggunakan algoritma Fuzzy C-Means (FCM) dan DBSCAN. FCM digunakan untuk mengelompokkan data berdasarkan kesamaan dengan pusat cluster, sementara DBSCAN mengidentifikasi kelompok berdasarkan kepadatan spasial. Kedua metode ini diharapkan menghasilkan clustering data yang lebih baik. Validasi SSE, Calinski-Harabasz Index (CH), Silhouette Coefficient, dan Davies Bouldin Index (DBI) digunakan untuk memperoleh nilai K optimal.Variabel LRFM digunakan untuk menggambarkan perilaku donatur selama berdonasi. Evaluasi menunjukkan SSE FCM: 4563140439.7347, CHI: 0.3128562606910544, SS: 0.08061440564597909, dan DBI: 9.910899528742423. Evaluasi DBSCAN menunjukkan CHI: 3237127.1389106703, SS: 0.8479515063332151, dan DBI: 2.1939426200975047. DBSCAN tampak memberikan hasil klasterisasi yang lebih baik, dengan pemisahan dan homogenitas klaster yang lebih baik dibandingkan FCM.
SISTEM PAKAR DIAGNOSA KERUSAKAN KOMPUTER DENGAN ALGORITMA CERTAINTY FACTOR PADA LAB ICT BUDI LUHUR Kalyzta, Juan; Syafrullah, Mohammad
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 6 No 1 (2023): Jurnal SKANIKA Januari 2023
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v6i1.2996

Abstract

Pada Proses Maintenance Asisten Lab biasanya mengidentifikasi Komputer satu persatu untuk mencari masalah yang ada pada setiap komputer ,Proses identifikasi yang berlangsung untuk mendiagnosa kerusakan masing masing Komputer dapat berlangsung cukup lama. Hal ini disebabkan terbatasnya pengetahuan dan keahlian untuk melakukan perbaikan terhadap gangguan atau kerusakan pada komputer. Sehingga menyebabkan penumpukan jumlah kerusakan pada Komputer di Laboratorium Universitas Budi Luhur, Kemajuan teknologi di bidang komputer saat ini sangat pesat, terutama di bidang kecerdasan tiruan, termasuk sistem pakar. Sistem pakar adalah cabang dari kecerdasan buatan dan bagian dari ilmu komputer. Sistem ini membantu mentransfer pengetahuan manusia ke komputer. Komputer menggabungkan basis pengetahuan dengan sistem penalaran untuk menggantikan peran para ahli dalam pemecahan masalah, berdasarkan kerusakan yang dihadapi sebelumnya, sistem pakar ini dibuat untuk membantu asisten memahami petunjuk kerusakan komputer yang ada dan kemungkinan solusi untuk memperbaiki kerusakan tersebut, pengembangan sistem pakar ini menggunakan metode algoritma Forward Chaining sebagai mesin inferensi untuk menentukan rule dan membuat Pohon Keputusan (Decision Tree) dan metode Algoritma Certainity Factor untuk menentukan nilai keyakinan diagnostik. Saat merancang sistem pakar ini, pengguna dapat memilih petunjuk kerusakan komputer, Output yang dihasilkan adalah tingkat kepercayaan, probabilitas kerusakan yang dialami, deskripsi solusi perawatan, dan probabilitas lain yang dialami oleh komputer. Hasil pengujian berdasarkan metodologi pengujian blackbox telah menghasilkan kinerja fungsional 100% sesuai dengan daftar persyaratan sistem. Tes akurasi menghasilkan nilai akurasi yang sangat baik. Ini adalah 100% dari 10 sampel data yang tersedia
CHATBOT DENGAN ALGORITMA MULTILAYER PERCEPTRON SEBAGAI LAYANAN INFORMASI SEKRETARIAT FAKULTAS TEKNOLOGI INFORMASI UNIVERSITAS BUDI LUHUR Maulidia, Mia; Syafrullah, Mohammad
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 7 No 1 (2024): Jurnal SKANIKA Januari 2024
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v7i1.3148

Abstract

Information service centers are very important today, with the existence of information service centers many people are helped in conveying and receiving the information needed, including in the world of higher education, information about academics is needed in teaching and learning activities in lectures. Although to get general information related to current teaching and learning activities at the Faculty of Information Technology at Budi Luhur University can be accessed through the website, supervisors and social media. But the problem now is to get information from supervisors who sometimes take too long to respond to students or even miss not responding. One way to overcome this problem is to create a chatbot system as an information service at the Faculty of Information Technology at Budi Luhur University using a natural language Natural Language Processing (NLP) approach with the Neural Network method and Multilayer Perceptron algorithm and extraction features using the binary Bag of words method by matching and giving a value of 1 to each question that is used as a token at the appropriate preprocessing stage on The train data and assigns a value of 0 to each toen that does not match the token on the train data. As well as using datasets saved in JSON format. Based on the model that has been trained to obtain accuracy and loss results with an accuracy value = 1,000 and a loss value = 0.0117, it can be concluded that the trained model is a good model.
Co-Authors Abdul Rahman Abdul Rahman Wahid Abhishek Abhishek Abhishek Singh Abhishek, Abhishek Achmad Maulana Achmad Solichin Adiyarta, Krisna Agarwal, Prachi Agarwal, Sonali Agarwal, Sonali Agarwal, Sonali Agung Darmawan Agus Riyanto Alvian Winata, Arif Andrico Andrico Anggraini, Triana Aria Mustofa Hidayat Armando Ondihon Kristoper Purba Arumgam, Yogesvaran Bayuaji, Luhur Darmawan, Agung Devit Setiono Dewi Kusumaningsih Dewi, Ernawati Dhannuri, Syam Prasad Dwi Pebranti Dwi Pebrianti Elizabeth Yohanes Emil Salim Ernawati Dewi Esti Setiasih Gaol, GA Monang Lumban Hadi Syahrial Hadjianto, Mardi Hanif, Raihan Labib Indra Riyanto Indra Riyanto Irawan Irawan Irawan Irawan Jamhari Jamhari Java, Muhammad Arya K Singh Kalyzta, Juan Kassim, Siti Rafidah Binti Krisna Adiyarta Kusumaningsih, Dewi Luhur Bayuaji M. Ivan Putra Eriansya Makhdum Rosadi Martono Martono Maulidia, Mia Meilieta Anggriani Porrie Mohammad Fadhil Abas Muhammad Azhar Mujahid Muhammad Azhar Rasyad Muhammad Hasanul Huda Mutiarawan, Rezza Anugrah Nagabhushan, P. Narinder Punn Nugraha Abdullah, Indra Nurnajmin Qasrina Ann Nurnajmin Qasrina Ann Ayop P. Nagabhushan Painem, Painem Pandu Pradinata Pebranti, Dwi Porrie, Meilieta Anggriani Prachi Agarwal Prasetiamaolana, Eko Pudoli, Ahmad Punn, Narinder Purba, Armando Ondihon Kristoper Purwanto Purwanto Qasrina Ann Ayop, Nurnajmin Rakesh Kumar Yadav Ramdhan, Syaipul Ratna Kusumawardani Ratna Kusumawardani, Ratna Rezza Anugrah Mutiarawan Rianto, Yan Ridho Saputra Rizki Aji Wibowo Roeswidiah, Ririt Rusdah Rusdah Ruwirohi, Jan Everhard S. Venkatesan Sadhana Tiwari Sambhavi Tiwari Samidi Samidi Sanjay Kumar Sonbhadra Sanjay Kumar Sonbhadra Sari, Widya Kumala Setyawan Widyartoh Shekhar Verma Shkehar Verma Singh, Abhishek Singh, K Siti Rafidah Binti Kassim Sonali Agarwal Sonali Agarwal Sonali Agarwal Sonbhadra, Sanjay Kumar Sonbhadra, Sanjay Kumar Sumarudin, Muhammad Supardi Supardi Supardi Supardi Supardi, Supardi Syaddad, Muhammad Sulthan Syaiful Anwar Syaipul Ramdhan Syam Prasad Dhannuri Thisa Tri Utami Tiwari, Sadhana Tiwari, Sambhavi Tutik Sri Susilowati Venkatesan, S. Verma, Shekhar Verma, Shkehar Victor Ilyas Sugara Widdy Chandra Permana Widya Kumala Sari Widyartoh, Setyawan Windarto, Windarto Yadav, Rakesh Kumar Yan Rianto Yodi Susanto Yogesvaran Arumgam Yulianawati Yulianawati Yulianawati Zulkarnaen Noor Syarif