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All Journal MAKNA: Jurnal Kajian Komunikasi, Bahasa dan Budaya PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmu Kepolisian Building of Informatics, Technology and Science Jurnal Ekonomi Manajemen Sistem Informasi Action Research Literate (ARL) Journal of Innovation Research and Knowledge Jurnal Riset sosial humaniora, dan Pendidikan (Soshumdik) Journal of Comprehensive Science Journal of Law, Poliitic and Humanities Jurnal Portofolio: Jurnal Manajemen dan Bisnis Jurnal Pengabdian Kepada Masyarakat Kalam
Jarot Prianggono
Sekolah Tinggi Ilmu Kepolisian
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Prediction of Theft with Machine Learning Technology at Police Station Hadmanto, Aditya; Prianggono, Jarot
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5107

Abstract

This study originated from the increase in theft cases in the jurisdiction of Banjarbaru District Police which resulted in material and psychological losses for victims and disturbed the overall sense of security of the community. The research aims to develop a method that can assist the police in preventing and tackling theft crimes more effectively using machine learning algorithms. Research methods include research design, quantitative approach, and data collection and analysis techniques. The data analyzed included various categories of relevant information, such as the victim's gender, age, occupation, location of the incident, as well as details related to the modus operandi and losses suffered by the victim. The main data used is data on victims of theft crimes in the Banjarbaru Police jurisdiction during the 2019-2023 period. Data collection was carried out using primary data available from Min Ops Reskrim Polresta Banjarbaru. using the K-Nearest Neighbor (KNN) and Naïve Bayes (NB) algorithms to process historical data on theft crimes in Banjarbaru. The results reveal the general characteristics of theft cases, including time patterns, locations, and modus operandi, and compare the effectiveness between KNN and NB algorithms in predicting theft crimes. The conclusions emphasize the potential of machine learning in identifying theft patterns and provide recommendations for further development to support better decision-making and planning of crime prevention strategies
Crime of theft prediction using Machine Learning K-Nearest Neighbour Algorithm at Polresta Bandar Lampung Hermawan, Febry; Prianggono, Jarot
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12422

Abstract

The era of the industrial revolution 4.0 is a time where cyber and physical technology collaborate. This study aims to predict the types of theft crimes that occur in the Bandar Lampung Police area with the K-Nearest Neighbor algorithm, evaluate the prediction results and profiling the prediction results carried out by Bandar Lampung Police investigators in efforts to prevent and handle criminal acts of theft in the jurisdiction of the Bandar Lampung Police Lampung. The approach was carried out using the quantitative method of the K-Nearest Neighbor algorithm using the Rapidminer application by utilizing 1671 police report data from the Bandar Lampung Police and a questionnaire survey method conducted on 49 police investigators from the Bandar Lampung Police. Data collection techniques are carried out in a valid and reliable manner as a support for predictive validity. Based on the results of the classification and questionnaire, it was found that the majority of victims of the crime of theft were adult men who did not have a job and lived in urban areas. It was found that the majority of thefts occurred in parking lots in urban areas on Monday morning where the perpetrators used tools and targeted moving objects by tampering with locks which caused losses of around 10-50 million rupiah. This type of theft is theft by weighting (CURAT) which applies to Article 363 of the Criminal Code. The prediction results show that the neighboring value (K) and the distribution ratio of training and testing data are K=3 and 7:3, respectively. Predictions using K values and data sharing ratios show a high level of accuracy, namely 99. 20%. The results of the questionnaire show results that are in line with the results of the classification with an accuracy rate of the actual data of 75. 7122%. So by increasing the understanding skills of Bandar Lampung Police investigators using technology to predict the crime of theft, the number of theft crimes can be reduced.
Classification of Traffic Violations Using the Naïve Bayes Algorithm at Padang City Police Febrera , Gerryliyus; Prianggono, Jarot
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.10018

Abstract

Traffic violations have been increasing each year. According to data from the Padang City Police from 2018 to 2023, there were 128,913 traffic violation cases. This is not a small number, and it is time for the police to start utilizing machine learning (ML) technology to evaluate traffic violation cases, as ML can identify hidden patterns or information that cannot be detected manually by conventional statistics or by traffic officers. This research aims to classify traffic violations using the Naïve Bayes algorithm at the Padang City Police by conducting evaluations and comparisons using different dataset ratios. The best algorithm obtained from the comparison will then be analyzed, and the research findings are expected to serve as a reference for the relevant authorities. This research is quantitative in nature, using an experimental method. The data sources or information were obtained from traffic ticket documentation at the Padang City Police and questionnaires distributed to traffic officers of the Padang City Police. The research results show that the Naïve Bayes (NB) algorithm can be used to classify traffic violations at the Padang City Police. The performance test results of the Naïve Bayes (NB) algorithm using all comparison algorithms with different training and testing dataset ratios resulted in 100% accuracy. However, during cross-validation, the Naïve Bayes algorithm achieved the highest accuracy only with training and testing dataset ratios of 80%:20% and 90%:10%. This is due to the large dataset size in this research, which is more than 100,000 entries. The evaluation results of the Naïve Bayes algorithm show that the best model is achieved with the Naïve Bayes algorithm using an 80% training and 20% testing dataset split. Although the performance is similarly high with a 90%:10% training and testing ratio, the researcher chose the 80%:20% training and testing ratio as the best algorithm for reasons of efficiency during training. The argument is that even with just 80%, it is able to predict/classify 20%, which is more efficient than training 90% to predict/classify 10%. Another finding from this implementation is that with a large dataset of 100,000 entries or more, high and stable performance can be achieved, so this research also suggests that to achieve good results from traffic violation classification, the dataset should be above 100,000 entries.
Prediksi Lokasi Tindak Pidana Pencurian Menggunakan Metode K-Nearest Neighbor di Wilayah Hukum Polres Badung Polda Bali Bayuna, Kadek Ari; Prianggono, Jarot; Wibowo, Didit Bambang
Jurnal Portofolio : Jurnal Manajemen dan Bisnis Vol. 4 No. 1 (2025): Prediksi dan Pemanfaatan Big Data Dalam Manajemen Cyber Digital Dunia Peradaban
Publisher : Prisani Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70704/jpjmb.v4i1.352

Abstract

This study aims to predict the location of theft crimes in the jurisdiction of the Badung Police by applying the K-Nearest Neighbor (KNN) method. The main focus of the study is to identify crime patterns based on time and location variables in order to improve the effectiveness of police prevention strategies. The problem raised is how machine learning-based models can help detect theft-prone areas and improve accuracy in crime prevention efforts. This study uses a quantitative approach with the CRISP-DM (Cross-Industry Standard Process for Data Mining) method. The data used includes information on time, location of the incident, and theft categories based on police reports. The research process includes business understanding, data exploration and preparation, modeling using KNN, model performance evaluation, and implementation in the form of interactive map visualization. Model performance is analyzed using evaluation metrics such as precision, recall, and F1-score to measure the level of prediction accuracy. The results of the study show that the KNN model is able to identify locations with a high risk of theft with fairly good accuracy. Areas with high activity, such as transportation facilities and commercial areas, are more vulnerable to this crime. In addition, thefts occur more often in the morning, evening, and early morning when people are off guard. In conclusion, the KNN method is effective in predicting theft-prone areas. Implementation of this model can help the police improve the effectiveness of patrols and security strategies. It is recommended that this model be combined with a geographic information system (GIS) to facilitate the analysis of crime patterns in order to improve public security more proactively.
Analisis Prediksi Tindak Pidana Pencurian Dengan Metode Klasifikasi Algoritma K-Nearest Neighbor di Polresta Bengkulu, Polda Bengkulu Kusuma, Firman Bagus Perdana; Wibowo, Didit Bambang; Prianggono, Jarot
Jurnal Portofolio : Jurnal Manajemen dan Bisnis Vol. 4 No. 1 (2025): Prediksi dan Pemanfaatan Big Data Dalam Manajemen Cyber Digital Dunia Peradaban
Publisher : Prisani Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70704/jpjmb.v4i1.354

Abstract

This research is driven by the phenomenon of high crime rates, especially theft. A method is needed that can assist the police in planning and implementing a more optimal and targeted patrol strategy. Data mining can be applied to crime data, especially theft crime data, to obtain information that can be used as a basis for decision making in carrying out patrols by the Police. Machine learning algorithms can be used to classify types of theft crimes based on characteristics and predict the possibility of future theft crimes based on influencing factors. Data mining is a logical process to find information that is very useful as a supporting tool in decision making. K-Nearest Neighbor (KNN) is a classification method and uses the CRISP-DM (Cross Industry Standard Process for Data Mining) framework in pulling information from a collection of datasets. This study uses a population in the form of Police Reports (LP) of Theft Victims that occurred in the Bengkulu Police Resort which occurred from 2020 to 2025. The K-Nearest Neighbor (KNN) model shows a fairly high level of reliability in predicting the time of theft, the age of the victim, and the type of item stolen at Bengkulu Police Resort. The KNN model was able to predict the time of incidents, victim age, and type of stolen items with high accuracy — 87.35%, 82.41%, and 88.43% respectively. Based on the results of this study, the application of machine learning in predictive policing can be implemented more effectively than conventional patrol methods. Therefore, the KNN prediction model developed in this study is recommended to be applied in the police patrol system, especially at Bengkulu Police Resort, to improve the effectiveness of surveillance and crime prevention in the jurisdiction of Bengkulu City.
Pengaruh Kompetensi Penyidik dan Kepercayaan Pada Kepemimpinan Terhadap Kinerja Anggota Satuan Reskrim Iswahyudi, Agung; Setiono, Joko; Prianggono, Jarot
Jurnal Portofolio : Jurnal Manajemen dan Bisnis Vol. 4 No. 1 (2025): Prediksi dan Pemanfaatan Big Data Dalam Manajemen Cyber Digital Dunia Peradaban
Publisher : Prisani Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70704/jpjmb.v4i1.356

Abstract

The research findings indicate that investigator competence has a significant influence on the performance of the Criminal Investigation Unit members of Tanah Laut Police Precinct. The multiple linear regression test results show that investigator competence (X1) has a regression coefficient of 1.525 with a significance value of 0.000 (< 0.05). The partial t-test results also confirm that investigator competence significantly affects the performance of unit members. Similarly, trust in leadership also has a significant influence on the performance of the Criminal Investigation Unit members of Tanah Laut Police Precinct. The multiple linear regression test results indicate that trust in leadership (X2) has a regression coefficient of 0.551 with a significance value of 0.000 (< 0.05). The partial t-test results further confirm that trust in leadership significantly affects the performance of unit members. Investigator competence and trust in leadership jointly have a significant influence on investigator performance. The multiple linear regression test results show that investigator competence (X1) has a regression coefficient of 1.525, and trust in leadership (X2) has a regression coefficient of 0.551, with a significance value of 0.000 (< 0.05), indicating that both variables simultaneously enhance investigator performance.The coefficient of determination (Adjusted R Square) test result of 0.856 indicates that 85.6% of the variation in investigator performance can be explained by investigator competence and trust in leadership, while the remaining 14.4% is influenced by other factors. The R-value of 0.927 demonstrates that the relationship between the two independent variables and investigator performance is very strong.
Prediksi Ancaman Yang Dihadapi KORPSBRIMOB Polri Dengan Menggunakan Metode K-Nearest Neighbor Classifier Machine Learning dan Naive Bayes Adi, Condro Purnomo; Setiono, Joko; Prianggono, Jarot
Jurnal Portofolio : Jurnal Manajemen dan Bisnis Vol. 4 No. 1 (2025): Prediksi dan Pemanfaatan Big Data Dalam Manajemen Cyber Digital Dunia Peradaban
Publisher : Prisani Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70704/jpjmb.v4i1.361

Abstract

This research specifically applies two classification algorithms—K-Nearest Neighbors (K-NN) and Naïve Bayes—to assess and predict social disturbances. The empirical findings indicate that, in the context of social factors, the Naïve Bayes algorithm achieved a classification accuracy of 93.91%, surpassing K-NN's 73.33%. These results suggest a significant likelihood of social disturbances during the 2025–2026 period. Such disturbances, often involving the Police Brimob Corps, are predominantly driven by social tensions, including intergroup conflict, economic grievances, dissatisfaction with governance, and identity-based strife.The prevalence of socially-rooted unrest underscores the need for multifaceted intervention strategies. As a specialized unit within Indonesia’s national security apparatus, the Brimob Corps is positioned to mitigate these threats through integrated approaches ranging from mass security and enforcement operations to intelligence-led dialogue and preventive engagement. This highlights the vital role of predictive analytics in shaping proactive and adaptive security policies.
Pengaruh Tindakan Disiplin, Pengawasan Internal dan Beban Kerja Terhadap Kinerja Anggota Polri di Polda Kepulauan Riau Mahendra, Nyoman Ananta; Prianggono, Jarot; Sinaga, Saut Panggabean; Dhamayanti, Sylvia Kartika
Jurnal Portofolio : Jurnal Manajemen dan Bisnis Vol. 4 No. 1 (2025): Prediksi dan Pemanfaatan Big Data Dalam Manajemen Cyber Digital Dunia Peradaban
Publisher : Prisani Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70704/jpjmb.v4i1.362

Abstract

One of the factors that can improve employee performance is motivation. Motivation is a series of attitudes and values that influence individuals to achieve specific things in accordance with individual goals. An employee will be motivated at work, if the employee is in a pleasant work environment and gets fair   number of maintenance factors is insufficient, it will cause job dissatisfaction. So the maintenance factor does not create job satisfaction but can prevent job dissatisfaction. This research was conducted by survey to a population of 376 Polri members in the Riau Islands Regional Police. Research with quantitative data processing and results show that there is an influence of disciplinary action to performance (89.72%), internal control to performance (8.88%) and workload to performance (4.6%). Thus it is clear that disciplinary action for police is the most important variable in improving performance in the field.
Penerapan Model Prediksi Untuk Diintegrasikan Dalam Program Analisis Kerja Personel Dalam Mendukung Peningkatan Perencanaan Strategis dan Operasional Kepolisian: di Polresta Bandar Lampung Polda Lampung Hermawan, Febry; Prianggono, Jarot; Hartanto, Dadang
Jurnal Portofolio : Jurnal Manajemen dan Bisnis Vol. 4 No. 2 (2025): Integrasi Teknologi Informasi dan Manajemen Operasional Kerja Lembaga
Publisher : Prisani Cendekia

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

Abstract

In the Indonesian context, the application of predictive technology in the police sector still faces various challenges. Some of them are low accuracy, in-date, and difficulty accessing data that results in government bureaucratic inefficiency. Bandar Lampung City is one of the regions in Indonesia with a high crime rate. Data from the Bandar Lampung Police shows that theft cases dominate crime reports in the area. In this study, the improvement of ML model performance is expected to provide more accurate predictions and support police officers in designing more appropriate strategies to tackle theft crimes This study uses two popular Machine Learning (ML) models, namely K-Nearest Neighbors (k-NN) and Naïve Bayes (NB), to analyze and predict theft crimes in the jurisdiction of the Bandar Lampung Police. The approach is carried out using a quantitative method of algorithm k-Nearest Neighbor and Naive Bayes using the Rapidminer application by utilizing 1671 data from the Bandar Lampung Police police report and a survey method with questionnaires. The data collection technique is carried out validly and reliably, then the police report data will be used for prediction and the questionnaire data will be used to support the validity of the prediction. Based on the results of comparative research conducted using the K-NN model and the Naive Bayes model, it is known that the k-NN model on theft victim data based on the type of theft that occurred is able to predict by 98.80% and for the Naive Bayes model is able to predict by 99.85%. And for suspect data in the k-NN model, it is predicted to be 70.00% while the Naive Bayes model predicts 88.00%. In predicting theft incidents in Bandar Lampung, the selection of the Naive Bayes (NB) model proved to be much more effective and had a very high accuracy compared to K-Nearest Neighbors (K-NN). Based on the test results, the Naive Bayes model provides a prediction accuracy of 99.85%, which is much better compared to K-NN which may not achieve the same level of accuracy.
Analisis Klasifikasi Kecelakaan Lalu Lintas di Polresta Bogor Kota Menggunakan Metode K-Nearest Neighbor (KKN) dan Naïve Bayes (NB) Oktania, Ziska; Prianggono, Jarot
Action Research Literate Vol. 9 No. 5 (2025): Action Research Literate
Publisher : Ridwan Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/arl.v9i5.2961

Abstract

Kecelakaan lalu lintas di Kota Bogor terus meningkat seiring pertumbuhan kendaraan dan urbanisasi. Penelitian ini menganalisis serta mengklasifikasikan kecelakaan di wilayah Polresta Bogor Kota menggunakan metode K-Nearest Neighbor (KNN) dan Naïve Bayes (NB) untuk mengidentifikasi pola berdasarkan lokasi, waktu kejadian, dan kondisi jalan. Hasilnya diharapkan menjadi dasar rekomendasi peningkatan keselamatan lalu lintas. Penelitian ini menggunakan pendekatan kuantitatif dengan metode data mining untuk mengolah data kecelakaan yang dihimpun Polresta Bogor Kota. KNN dan NB dibandingkan dalam klasifikasi kecelakaan berdasarkan jenis, kondisi jalan, dan waktu kejadian, dengan evaluasi akurasi dan efektivitas model. Hasil menunjukkan KNN lebih unggul dengan akurasi di atas 91%. Kecelakaan ringan paling sering terjadi, sementara kecelakaan berat lebih banyak ditemukan pada Selasa dan Minggu, terutama di jalan utama seperti Jalan Provinsi dan Jalur Mudik. Kesimpulan penelitian ini menegaskan bahwa KNN lebih efektif dibandingkan NB dalam klasifikasi kecelakaan. Studi lanjutan disarankan mengeksplorasi algoritma lain, seperti Random Forest atau XGBoost, serta memperkaya data dengan faktor tambahan, seperti penggunaan seatbelt dan kondisi lalu lintas, guna meningkatkan akurasi dan kedalaman analisis.
Co-Authors Abdul Rachman Virga Maulidhany Yusuf Adi, Condro Purnomo Anas, Hanifah Arnie Herdiyathi Bayuna, Kadek Ari Dax E.S. Manupputy Febrera , Gerryliyus Firmansyah, Adimas Hadmanto, Aditya Halimah Hartanto, Dadang Hermawan, Febry Heru Adrian Ilham Prisgunanto Iswahyudi, Agung JOKO SETIONO Kusuma, Firman Bagus Perdana Mahendra, Nyoman Ananta Manupputy, Dax E.S. Muhammad Gifari Syarifuddin Nailul Khikam Nandito Marbun Novi Indah Earlyanti Oktania, Ziska Prianggono, Halimah Safii, Mifta Hadi Setyadi, Rangga Sinaga, Saut Panggabean Sylvia Kartika Dhamayanti Wibowo, Didit Bambang Wulan Utami, Rahmawati