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Determination of Business Location by Using Analytical Hierarchy Process (AHP) and Weighted Product (WP) Embun Fajar Wati; Elvi Sunita Perangin-Angin
IJISTECH (International Journal of Information System and Technology) Vol 6, No 3 (2022): October
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i3.253

Information technology that is currently developing provides opportunities for business actors to develop their business. One of the factors that make a business grow is the location of the business. It is not easy to determine the appropriate business location, so various selections are needed so as to be able to measure the feasibility of the location. The existence of a decision support system can assist in making decisions about determining the location. The method chosen is the AHP method combined with the WP method. To get the value calculated by the AHP method, data collection by interview and observation was used. The literature study is used in the calculation stages with the AHP and WP methods. The use of a combination of AHP and WP methods in determining the location of the business gives a ranking result, with the highest score achieved by the Royal location of 0.617 and the lowest value achieved by the Poris location of 0.094. After observing the new location, Royal for 3 months, there was an increase in sales in the first month by 3 million/15%, in the 2nd month by 4 million/19% and in the 3rd month by 7 million/30%

Determination of Business Location by Using Analytical Hierarchy Process (AHP) and Weighted Product (WP) Embun Fajar Wati; Elvi Sunita Perangin-Angin
IJISTECH (International Journal of Information System and Technology) Vol 6, No 3 (2022): October
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i3.253

Information technology that is currently developing provides opportunities for business actors to develop their business. One of the factors that make a business grow is the location of the business. It is not easy to determine the appropriate business location, so various selections are needed so as to be able to measure the feasibility of the location. The existence of a decision support system can assist in making decisions about determining the location. The method chosen is the AHP method combined with the WP method. To get the value calculated by the AHP method, data collection by interview and observation was used. The literature study is used in the calculation stages with the AHP and WP methods. The use of a combination of AHP and WP methods in determining the location of the business gives a ranking result, with the highest score achieved by the Royal location of 0.617 and the lowest value achieved by the Poris location of 0.094. After observing the new location, Royal for 3 months, there was an increase in sales in the first month by 3 million/15%, in the 2nd month by 4 million/19% and in the 3rd month by 7 million/30%

Pregnancy Disease Diagnostic Expert System With Certainty Factor Method Embun Fajar Wati; Elvi Sunita Perangin-Angin; Budi Sudrajat
IJISTECH (International Journal of Information System and Technology) Vol 6, No 6 (2023): April
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/.v6i6.292

Lack of knowledge and information about diseases in pregnancy can delay pregnant women from knowing there are diseases in their pregnancy. Diseases that attack a woman's womb need to be examined by an expert, while experts for this disease are still rare and require a lot of money. In order for the initial diagnosis to be carried out by pregnant women, a solution is proposed in the form of an expert system for diagnosing pregnant women's diseases using the Certainty Factor (CF) method based on the symptoms felt by pregnant women. The research stages used in this study used 4 steps, namely data collection consisting of disease data and symptom data, disease data and symptoms, as well as patient data, symptoms and weights, data analysis using the certainty factor method, validation and evaluation. Diagnostic results that are not in accordance with the CF calculation of around 37.5%, while the results in accordance with the CF calculation were 11 patients or 62.5%.

Pregnancy Disease Diagnostic Expert System With Certainty Factor Method Embun Fajar Wati; Elvi Sunita Perangin-Angin; Budi Sudrajat
IJISTECH (International Journal of Information System and Technology) Vol 6, No 6 (2023): April
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/.v6i6.292

Lack of knowledge and information about diseases in pregnancy can delay pregnant women from knowing there are diseases in their pregnancy. Diseases that attack a woman's womb need to be examined by an expert, while experts for this disease are still rare and require a lot of money. In order for the initial diagnosis to be carried out by pregnant women, a solution is proposed in the form of an expert system for diagnosing pregnant women's diseases using the Certainty Factor (CF) method based on the symptoms felt by pregnant women. The research stages used in this study used 4 steps, namely data collection consisting of disease data and symptom data, disease data and symptoms, as well as patient data, symptoms and weights, data analysis using the certainty factor method, validation and evaluation. Diagnostic results that are not in accordance with the CF calculation of around 37.5%, while the results in accordance with the CF calculation were 11 patients or 62.5%.

Edukasi Literasi Digital terhadap Perkembangan Anak pada TPA Al Ihsan Embun Fajar Wati; Anggi Puspita Sari
SENADA : Semangat Nasional Dalam Mengabdi Vol. 2 No. 1 (2021): SENADA: Semangat Nasional Dalam Mengabdi
Publisher : Politeknik Bina Madani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56881/senada.v2i1.82

Teknologi digital sudah menyebar ke seluruh lapisan masyarakat tetapi sebagian besar masyarakat belum mampu menggunakan teknologi tersebut secara baik khususnya terhadap anak-anak. Penggunaan teknologi digital yang tidak tepat yang diberikan kepada anak-anak bisa menimbulkan efek yang tidak baik bagi kelangsungan kehidupan individu dan sosial mereka. Oleh sebab itu literasi digital selayaknya diberikan edukasi agar dapat mendidik kepribadian bangsa terutama generasi penerus bangsa. Literasi digital merupakan dasar pengetahuan yang didukung oleh teknologi informasi yang saling terhubung dengan tujuan untuk memahami bagian-bagian penting dalam literasi digital dan prosedur literasi terhadap anak. Untuk itu kami dari tim Dosen Fakultas Teknik dan Informatika akan mengadaan pengabdian masyarakat mengenai literasi digital di TPA Al Ihsan Duri Kosambi Jakarta Barat dengan harapan pada kegiatan ini, anak-anak mampu menggunakan teknologi digital dengan baik dan benar, mampu memilih mana yang baik dan mana yang buruk. Adapun metode pelaksanaan kegiatan ini dimulai dari persiapan dan pelaksanaan. Persiapan dimulai dari observasi lokasi, wawancara, sampai mencari referensi untuk persiapan acara. Kemudian pelaksanaan nya dilakukan dengan cara memaparkan materi melalui video anak-anak yang disesuaikan dengan tema, storytelling dan diakhiri dengan pemberian games. Kegiatan ini juga akan dipublikasikan dalam bentuk press release di media online.

Improved Naive Bayes Algorithm with Particle Swarm Optimization to Predict Student Graduation Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Sari, Anggi Puspita
IJISTECH (International Journal of Information System and Technology) Vol 7, No 6 (2024): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i6.338

Timely graduation is very important for educational institutions such as universities, especially for students. Because it can prove that the University and students are able to undergo the learning process theoretically and practically. But many students do not pay attention to graduation, especially those who are already working or married. Therefore, analysis is needed to predict student graduation so that solutions can be found by the University. Data mining was chosen as a method to process data to get new information. The algorithm used in data mining is Naïve Bayes. The research stages include loading data into excel, cleaning empty data, selecting databases related to graduation and taking data from 300 students majoring in Informatics Engineering. The next stage is data transformation by categorizing student data, namely personal data attributes (gender, age, marital status, job status) and academic data (grade). Data testing, application of Naïve Bayes algorithm and accuracy testing were carried out with Rapis Miner software version 10.3.001. The results of data processing with Rapid Miner using the Naïve Bayes algorithm are shown with the Confusion Matrix and ROC Curve. The results of confusion matrix from data processing with Naïve Bayes in the form of accuracy, precision, and recall have the same result of 100%. The percentage of the Confusion Matrix indicates that the model created can classify correctly and accurately. The ROC curve depicted with AUC yields a value of 1, which means that the test showed excellent results

Modelling of C4.5 Algorithm for Graduation Classification Wati, Embun Fajar; Sudrajat, Budi; Nasution, Raudah
IJISTECH (International Journal of Information System and Technology) Vol 8, No 1 (2024): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i1.345

Student admissions in universities every year become a routine thing to do, some even do student admissions every semester. That way, the number of students will continue to grow. Especially if there are students who graduate late, it will increase the number of students in the university. There are many things that can affect graduation, namely personal data (gender, age, marital status, job status) and academic data (grade). Before making a decision, universities must analyze the number of students and the factors that most influence student graduation. Analysis by classifying graduation using C4.5 algorithms. The research method used consists of selection to ensure the data used in the KDD process is appropriate and quality data. Then preprocessing by means of data cleaning, data reduction, and data normalization. The next method is transformation for age attributes to young and old, grade attributes to large and small. The last method is C4.5 algorithm modeling with rapid miner and evaluation. Through the calculation process using the classification method and C4.5 algorithm with the attributes described earlier, the results were obtained that the accuracy of the graduation classification reached 97.00%, the precision value was 91.79%, and the recall value was 100.00%, and the AUC value was 0.978. This means that the model has a very high level of accuracy and has an excellent ability to separate samples from both target classes.

Prediction of Student Graduation using the K-Nearest Neighbors Method Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Sari, Anggi Puspita
IJISTECH (International Journal of Information System and Technology) Vol 7, No 3 (2023): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i3.318

Predictions on the accuracy of student graduation are designed to support study programs in guiding students so that they can graduate on time. The number of student graduations will influence the university's accreditation score. Graduation predictions can provide very useful information in decision-making; therefore, research was conducted on student graduation data. This data will be processed using the K-Nearest Neighbor method. The dataset used consisted of 150 students majoring in informatics engineering. The variables included gender, age, marital status, grade, and job status. The research methodology used in this study consists of 6 stages: Data Collection, Data Selection, Preprocessing, Transformation, Testing, and Evaluation. In the preprocessing or cleaning stage, the data can be fully utilized because all fields have been filled in correctly. Meanwhile, in the transformation stage, the data is categorized as follows: age (young: 19-24, old: 25-50) and grade (large: 3-4, small: 1-2.9). The K-Nearest Neighbor (KNN) method can predict student graduation rates. The KNN method, processed with the RapidMiner 9.9 tool, obtained an average accuracy of 100%. Based on the results of 100% accuracy and an AUC value of 1, it can be concluded that the KNN method is highly accurate in classifying graduation data for the 150 students.

Modelling of C4.5 Algorithm for Graduation Classification Wati, Embun Fajar; Sudrajat, Budi; Nasution, Raudah
IJISTECH (International Journal of Information System and Technology) Vol 8, No 1 (2024): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i1.345

Student admissions in universities every year become a routine thing to do, some even do student admissions every semester. That way, the number of students will continue to grow. Especially if there are students who graduate late, it will increase the number of students in the university. There are many things that can affect graduation, namely personal data (gender, age, marital status, job status) and academic data (grade). Before making a decision, universities must analyze the number of students and the factors that most influence student graduation. Analysis by classifying graduation using C4.5 algorithms. The research method used consists of selection to ensure the data used in the KDD process is appropriate and quality data. Then preprocessing by means of data cleaning, data reduction, and data normalization. The next method is transformation for age attributes to young and old, grade attributes to large and small. The last method is C4.5 algorithm modeling with rapid miner and evaluation. Through the calculation process using the classification method and C4.5 algorithm with the attributes described earlier, the results were obtained that the accuracy of the graduation classification reached 97.00%, the precision value was 91.79%, and the recall value was 100.00%, and the AUC value was 0.978. This means that the model has a very high level of accuracy and has an excellent ability to separate samples from both target classes.

Prediction of Student Graduation using the K-Nearest Neighbors Method Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Sari, Anggi Puspita
IJISTECH (International Journal of Information System and Technology) Vol 7, No 3 (2023): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i3.318

Predictions on the accuracy of student graduation are designed to support study programs in guiding students so that they can graduate on time. The number of student graduations will influence the university's accreditation score. Graduation predictions can provide very useful information in decision-making; therefore, research was conducted on student graduation data. This data will be processed using the K-Nearest Neighbor method. The dataset used consisted of 150 students majoring in informatics engineering. The variables included gender, age, marital status, grade, and job status. The research methodology used in this study consists of 6 stages: Data Collection, Data Selection, Preprocessing, Transformation, Testing, and Evaluation. In the preprocessing or cleaning stage, the data can be fully utilized because all fields have been filled in correctly. Meanwhile, in the transformation stage, the data is categorized as follows: age (young: 19-24, old: 25-50) and grade (large: 3-4, small: 1-2.9). The K-Nearest Neighbor (KNN) method can predict student graduation rates. The KNN method, processed with the RapidMiner 9.9 tool, obtained an average accuracy of 100%. Based on the results of 100% accuracy and an AUC value of 1, it can be concluded that the KNN method is highly accurate in classifying graduation data for the 150 students.

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