Garuda - Garba Rujukan Digital

OPTIMASI PENGGUNAAN MEDIA SOSIAL SEBAGAI ALAT PROMOSI DALAM KONTEKS DIGITAL MARKETING PADA UMKM BORNEO ISTIMEWA Mustopa, Ali; Lisnawanty; Sa'adah, Rabiatus; Dahlia, Rizka
Jurnal Penelitian dan Pengabdian Masyarakat Jotika Vol. 3 No. 2 (2024): Februari
Publisher : Jotika English and Education Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56445/jppmj.v3i2.131

Media sosial, sebagai platform online, memberikan kemudahan dalam berbagi konten dan berinteraksi. Sebagai alat promosi, media sosial menawarkan keuntungan meningkatkan visibilitas produk dan interaksi dengan target audiens. Dengan strategi yang tepat, media sosial dapat menjadi alat pemasaran yang efektif. Keberadaan media sosial memungkinkan interaksi lebih luas, memperluas jejaring, dan mengatasi kendala jarak serta waktu. Meski demikian, analisis menunjukkan bahwa sebagian besar anggota UMKM Borneo Istimewa masih kurang memahami potensi media sosial. Ketidakpahaman ini dapat mengakibatkan kurangnya pengenalan produk, kesulitan dalam pemasaran, dan hambatan promosi. Pemanfaatan media sosial menjadi solusi untuk meningkatkan visibilitas produk, memperluas pasar, dan mengatasi kendala promosi. Oleh karena itu, pelatihan mengenai pemanfaatan media sosial menjadi krusial untuk meningkatkan pemahaman dan keterampilan anggota UMKM, diharapkan membawa dampak positif pada pertumbuhan dan keberlanjutan UMKM Borneo Istimewa

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN CALON TENAGA KERJA DI KOTA PONTIANAK DENGAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) Lisnawanty, Lisnawanty; Dina, Fara; Sihombing, Daniel Oktodeli
Jurnal Pilar Nusa Mandiri Vol 14 No 2 (2018): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Labor is an important part that supports operations within a company. Manpower requirements planning is part of the recruitment program preparation activities to do selection so as to obtain workers who meet the qualifications in a company. However, not all selection processes succeed by getting candidates for work that are in line with company qualifications. Therefore, this study discusses the Decision Support System for the Selection of Prospective Workers in Pontianak City (SIPEKERJA) which is web-based. The method used in this study is the Simple Additive Weighting (SAW) method. Variables that become the main criteria in the Decision Support System for Selection of Prospective Laborers include gender, age, education level, field of expertise, and work experience. The system that has 3 levels of access (they are admin, prospective workers, and companies) is expected to be able to support the company in determining prospective workers according to the criteria expected by the company to occupy a certain part of the company.

Optimalisasi Pengelolaan Keuangan Perusahaan Konsultan Teknik (Studi Kasus CV. Citra Stapaka Sejahtera) Alfi Azhari, Nourmala; Lisnawanty, Lisnawanty; Ardiyansyah, Ardiyansyah; Irmayani, Windi
Jurnal Sistem Informasi Akuntansi Vol 4 No 2 (2023): Periode September 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/justian.v4i2.2971

Financial data management at an engineering consulting company, namely CV. Citra Stapaka Sejahtera, currently uses Microsoft Excel. The observation results show that the management of financial data is still simple, resulting in a long time for presenting financial report information, less efficient processes, and errors that often occur in financial report information. To overcome this problem, this research aims to develop a financial management application using the prototype method. The system developed allows Admins and Leaders to access various features such as managing project master data, income and expenses, carrying out transactions, and producing various reports such as project reports, RAB, income, expenses and profit and loss, both for projects and general. The hope is that the implementation of this system can improve financial management performance at CV. Image of Stapaka Sejahtera.

Sistem Informasi Pengelolan Laporan Keuangan Berbasis Web Pada CV. Damar Abadi thahir, anna; Lisnawanty, Lisnawanty
Jurnal Sistem Informasi Akuntansi Vol 5 No 1 (2024)
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/justian.v5i1.5057

CV. Damar Abadi transaction data processing and financial reports still use a manual system, namely recording still uses manual writing even for writing reports recorded in the ledger. The purpose of this study is to design a web-based cash receipt and disbursement management information system using the waterfall model for software development. Data collection techniques used are observation, interviews and literature studies. This application can help CV. Damar Abadi in managing inventory data, sales and purchase transactions, to the process of making financial reports automatically. This is very helpful for the company in managing its business.

Prediction of Obesity Categories Based on Physical Activity Using Machine Learning Algorithms Muhammad Iqbal; L, Lisnawanty; Steven Dharmawan, Weiskhy; Septian, Rendi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4053

Obesity is a global health issue with rising prevalence, marked by excessive fat accumulation that poses health risks. Contributing factors include poor eating habits, lack of physical activity, and genetics, which elevate the risk of chronic diseases like type 2 diabetes, heart disease, stroke, and cancer. This study examines an obesity dataset with seven variables: Age, Gender, Height, Weight, BMI, Physical Activity Level, and Obesity Category. The analysis reveals strong correlations between Body Weight, BMI, and the Obesity Category, while Body Height shows a moderate negative correlation. Various machine learning algorithms were tested, including XGBoost, AdaBoost, Gradient Boosting, and Extra Trees Classification. XGBoost emerged as the top performer, achieving the highest accuracy (0.9961) and an almost perfect AUC (0.9992), making it highly effective for obesity prediction. The study's significance lies in its ability to elucidate the key factors contributing to obesity and their interactions. By recognizing the strong links between Body Weight, BMI, and Obesity Category, healthcare professionals can craft more targeted interventions. Furthermore, the successful application of advanced machine learning algorithms underscores the potential for technology to enhance predictive accuracy and support healthcare decision-making. The findings highlight XGBoost's superior performance, demonstrating its value in predicting obesity and aiding in early diagnosis and prevention strategies. This research emphasizes the critical role of data and technology in tackling obesity and improving public health outcomes.

SISTEM INFORMASI PENDIDIKAN ANDALAN DAN INOVATIF (SI-PANDAI) GUNA MENINGKATKAN EFEKTIVITAS PEMBELAJARAN SEKOLAH PADA SMAN 1 BATU AMPAR Maulana, Reza; Lisnawanty, Lisnawanty
JUSIM (Jurnal Sistem Informasi Musirawas) Vol 9 No 2 (2024): JUSIM : Jurnal Sistem Informasi Musi Rawas DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusim.v9i2.2360

Penggunaan teknologi informasi dalam pendidikan telah berkembang pesat, memungkinkan inovasi dalam pembelajaran. Penelitian ini bertujuan mengembangkan Sistem Pendidikan Andalan dan Inovatif (SIPANDAI), sebuah platform pembelajaran berbasis web yang dirancang untuk meningkatkan efektivitas pembelajaran di Sekolah Menengah Atas (SMA). SIPANDAI diharapkan mampu meningkatkan aksesibilitas siswa dan guru, serta memfasilitasi pembelajaran jarak jauh yang fleksibel. Metode yang digunakan dalam pengembangan sistem ini melibatkan bahasa pemrograman PHP dengan kerangka kerja CodeIgniter 3 dan Bootstrap, yang mendukung tampilan website responsif di berbagai perangkat. Pengumpulan data dilakukan melalui observasi dan diskusi dengan para pemangku kepentingan di sekolah untuk merancang fitur-fitur yang mendukung pembelajaran lebih efektif. Beberapa fitur unggulan SIPANDAI antara lain materi pembelajaran yang dapat diakses kapan saja dan di mana saja, serta kemudahan dalam melakukan evaluasi dan interaksi antara siswa dan guru secara digital. SIPANDAI telah diimplementasikan di SMAN 1 Batu Ampar sebagai uji coba awal. Hasilnya menunjukkan bahwa platform ini membantu meningkatkan motivasi belajar siswa serta memberikan pengalaman belajar yang lebih terstruktur dan efisien dibandingkan metode pembelajaran konvensional.

Prediction of Obesity Categories Based on Physical Activity Using Machine Learning Algorithms Iqbal, Muhammad; L, Lisnawanty; Steven Dharmawan, Weiskhy; Septian, Rendi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4053

Obesity is a global health issue with rising prevalence, marked by excessive fat accumulation that poses health risks. Contributing factors include poor eating habits, lack of physical activity, and genetics, which elevate the risk of chronic diseases like type 2 diabetes, heart disease, stroke, and cancer. This study examines an obesity dataset with seven variables: Age, Gender, Height, Weight, BMI, Physical Activity Level, and Obesity Category. The analysis reveals strong correlations between Body Weight, BMI, and the Obesity Category, while Body Height shows a moderate negative correlation. Various machine learning algorithms were tested, including XGBoost, AdaBoost, Gradient Boosting, and Extra Trees Classification. XGBoost emerged as the top performer, achieving the highest accuracy (0.9961) and an almost perfect AUC (0.9992), making it highly effective for obesity prediction. The study's significance lies in its ability to elucidate the key factors contributing to obesity and their interactions. By recognizing the strong links between Body Weight, BMI, and Obesity Category, healthcare professionals can craft more targeted interventions. Furthermore, the successful application of advanced machine learning algorithms underscores the potential for technology to enhance predictive accuracy and support healthcare decision-making. The findings highlight XGBoost's superior performance, demonstrating its value in predicting obesity and aiding in early diagnosis and prevention strategies. This research emphasizes the critical role of data and technology in tackling obesity and improving public health outcomes.

Sistem Informasi Pengelolan Laporan Keuangan Berbasis Web Pada CV. Damar Abadi thahir, anna; Anna, Anna; Lisnawanty, Lisnawanty
Jurnal Sistem Informasi Akuntansi Vol. 5 No. 1 (2024): Periode Maret 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/justian.v5i1.5057

CV. Damar Abadi transaction data processing and financial reports still use a manual system, namely recording still uses manual writing even for writing reports recorded in the ledger. The purpose of this study is to design a web-based cash receipt and disbursement management information system using the waterfall model for software development. Data collection techniques used are observation, interviews and literature studies. This application can help CV. Damar Abadi in managing inventory data, sales and purchase transactions, to the process of making financial reports automatically. This is very helpful for the company in managing its business.

Optimalisasi Prediksi Dalam Kelulusan Berbasis Deep Learning: Perbandingan Kinerja Multi-Layer Perceptron dan Deep Neural Network Dewi, Yumi Novita; Iqbal, Muhammad; Lisnawanty; Maisyaroh; Suhardjono
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30756

Predicting on-time graduation is one of the significant challenges in education, aiming to model the factors influencing academic success. This study aims to compare the performance of two Deep Learning algorithms, namely Deep Neural Networks (DNN) and Multi-Layer Perceptron (MLP), in predicting on-time graduation. The methodology used involves evaluating both algorithms with various performance metrics, including Recall, Accuracy, Precision, AUC, MCC, and Cohen Kappa. The results show that DNN performs better in terms of Recall (0.9766), indicating its ability to capture most of the students who graduate on time, although its AUC (0.8625) and Precision (0.8803) are lower compared to MLP. On the other hand, MLP excels in Accuracy (0.8812) and Precision (0.9037), providing more stable results for MCC and Cohen Kappa, demonstrating a better balance in predicting students who graduate on time and those who do not. Overall, while DNN is more sensitive in capturing students who graduate on time, MLP performs better in terms of balance between accuracy and minimizing prediction errors. This study suggests using MLP if the primary priority is accuracy and prediction stability, while DNN is more suitable when the main focus is capturing as many students as possible who graduate on time.

Pemanfaatan AI dalam Pembuatan Media Pembelajaran untuk Guru Paud (HIMPAUDI) Kabupaten Kubu Raya Maulana, Reza; Firmansyah, Yoki; Lisnawanty; Mustopa, Ali
JURNAL ABDIMAS MADUMA Vol. 4 No. 3 (2025): Oktober, 2025
Publisher : English Lecturers and Teachers Association (ELTA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52622/jam.v4i3.535

Di era digital saat ini, anak-anak usia dini semakin terpapar dengan teknologi dan media sosial. Fenomena brain rot yang ditandai dengan konsumsi konten dangkal dan adiktif menjadi isu serius yang mengancam kemampuan berpikir kritis serta kesehatan mental anak. Masalah ini juga dihadapi oleh Himpunan Guru PAUD (HIMPAUDI) Kabupaten Kubu Raya. Keterbatasan penguasaan teknologi, terutama dalam pemanfaatan kecerdasan buatan (AI), menjadi kendala utama para guru PAUD. Sebagai solusi, dilakukan edukasi melalui workshop untuk mengajarkan para guru PAUD dalam pembuatan video pembelajaran yang edukatif dan menarik dengan memanfaatkan teknologi AI. Workshop ini dirancang aplikatif dan kontekstual agar guru dapat langsung mempraktikkan penggunaan AI dalam pembelajaran, serta melibatkan 40 guru PAUD. Untuk mengukur efektivitas kegiatan, digunakan instrumen angket pre-test dan post-test. Hasil survei menunjukkan bahwa semua peserta memiliki pandangan positif terhadap kegiatan ini, dengan 100% dari mereka setuju atau sangat setuju bahwa kegiatan tersebut memberikan pemanfaatan ilmu pengetahuan dan teknologi kepada peserta. Luaran dari kegiatan ini adalah kemampuan guru dalam menghasilkan video pembelajaran berbasis AI. Kata Kunci : Anak Usia Dini; Kecerdasan Buatan; Guru PAUD; Media

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