Recording and monitoring rabbit reproduction data at Balai Penelitian Ternak (Balitnak) are yet integrated using systematic recording and searching system approach. Thus the purpose of this study is to build an interactive web monitoring and recording rabbit reproduction system as well as implementing the Knuth Morris Pratt (KMP) algorithm in order to provide a reliable search function. This research produced an interactive monitoring of recording rabbit reproduction data which record important information about rabbit codes, dates (mating, palpation, childbirth, 21 days, 35 days), weight (mating, palpation, after giving birth, 21 days, 35 days ), the number of children born alive or dead (giving birth, 21 days, 35 days). The results of the implementation of the KMP algorithm generated a search with a time of 0.015095 milliseconds with an algorithm test based on the search for rabbit names as many as 20 types of rabbits.

Dictionary of medicine in the form of a thick book has many disadvantages, one of which is impractical. This is the reason for Indonesian developers to create drugs e-Dictionary. But the drugs e-Dictionary that has been developed is still in the form of a letter index so that users must search the terms one by one in sequential order. This has become so inefficient and ineffective that it is necessary to add a search function and query suggestion feature to the drug e-dictionary. The purpose of this study is to build a query suggestion facility on drugs e-Dictionary using the Levenshtein Distance algorithm. The stages of this research consist of the Development of web-based drugs e-Dictionary, Implementation of the Levenshtein Distance Algorithm, Query Suggestion Testing, and Usage. Based on the results of the implementation of the Levenshtein Distance algorithm and test results, Drugs e-Dictionary can evaluate words that are not in the database. The query suggestion function works by producing the closest word output contained in the database.

<p class="Abstrak">Selama kehamilan, seorang ibu sering mengalami mual, muntah, sakit punggung, atau indikasi penyakit ringan lainnya. Terkadang hal ini menghasilkan keputusan untuk minum obat tanpa resep dokter atau bidan. Perilaku seperti itu dapat menyebabkan risiko cacat janin. Hal ini dapat terjadi karena kurangnya pengetahuan tentang obat-obatan yang dapat dikonsumsi selama hamil. Oleh karena itu, diperlukan suatu sistem yang mengumpulkan pengetahuan tentang obat-obatan yang aman untuk dikonsumsi wanita hamil. Penelitian ini bertujuan untuk membangun Sistem Manajemen Pengetahuan (KMS) obat berbasis Android untuk wanita hamil. Penelitian ini menggunakan metode <em>Knowledge Management System Life Cycle</em> (KMSLC) yang terdiri atas beberapa tahap, yaitu evaluasi infrastruktur, pembentukan tim,  menangkap pengetahuan, merancang cetak biru<em> </em> KMS, verifikasi dan validasi, implementasi KMS, dan pengujian KMS. Penelitian ini telah menghasilkan pengetahuan <em>tacit</em> dan <em>explicit</em> mengenai obat ibu hamil. KMS Obat Ibu Hamil ini dilengkapi dua fungsi pencarian, yaitu pencarian berdasarkan nama obat dan pencarian berdasarkan keluhan nyeri atau indikasi penyakit. Pengetahuan Obat yang ada di KMS telah diverifikasi dan divalidasi. Pengembangan KMS Obat telah dilengkapi fiturnya sesuai dengan hasil proses penangkapan pengetahuan dari pakar. Adapun KMS Obat Ibu Hamil lebih lengkap 72% fiturnya dibandingkan dengan aplikasi yang telah berjalan, yakni MomsMed. KMS ini telah diuji fungsionalitas dan kompatibilitasnya, sehingga berfungsi dan kompatibel untuk versi Android 5.0, Lollipop (API level 21) ke atas. Terakhir, KMS ini dapat membantu ibu hamil dalam mencari pengetahuan tentang keamanan obat yang akan dikonsumsi ibu hamil sehingga tidak berisko pada janin.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p><em>During pregnancy, a mother often experiences nausea, vomiting, back pain, or other indications of minor illness. Sometimes this results in the decision to take medication without a prescription from a doctor or midwife. Such behavior can cause the risk of fetal defects. This can occur due to a lack of knowledge about medicines that can be consumed during pregnancy. Therefore, we need a system that collects knowledge about medicines that are safe for consumption by pregnant women. This study aims to build an Android-based Knowledge Management System (KMS) of medicines for pregnant women. This research uses the Knowledge Management System Life Cycle (KMSLC) method which consists of several stages, namely evaluation of infrastructure, team building, knowledge capture, design of the KMS blueprint, verification and validation, implementation of KMS, and KMS testing. This study has produced tacit and explicit knowledge regarding the medicines of pregnant women. KMS for Pregnant Women Medicines is equipped with two search functions, namely searching by medicine name and searching based on complaints of pain or indication of disease. Drug knowledge in KMS has been verified and validated. The development of KMS has been equipped with features under the results of the process of capturing knowledge from experts. The KMS for Pregnant Women is 72% more complete than the existing application, namely MomsMed. This KMS has been tested for functionality and compatibility, so it works and is compatible with Android versions 5.0, Lollipop (API level 21) and above. Finally, this KMS can assist pregnant women in seeking knowledge about the safety of drugs that are to be consumed so that there is no risk of the fetus.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

The Dictionary of Medicine in the form of a physical book has many drawbacks, one of them is its thickness makes it impractical to be carried. This becomes a motivation to develop drug dictionary applications in the form of a Drugs e-Dictionary. One of the developed Drugs e-Dictionary uses A-Z index-based approach to discover any drug terms. This approach is less effective and less efficient timewise. Therefore, it is necessary to add a search function that has an autocorrect feature to aid the user. The purpose of this study is to build a search module that has an autocorrect feature on Drugs e-Dictionary using the Levenshtein Distance algorithm. The methodology or the stages of this research divided into the construction of a search module on Drugs e-Dictionary, implementation of the Levenshtein Distance algorithm, and autocorrect validation test. The results of the algorithm implementation show that the search module with the autocorrect feature can detect typing errors in the inputted terms by producing the closest drug term output in the database, then automatically provide suggestions for improvement and display the results of the improved drug terms to the user. it reaches 90% accuracy of inputted query, with 90% precision and 90% recall.

Di era Pandemi Covid-19 ini, para orangtua yang memasukan anaknya di sekolah PAUD diharuskan mendampingi anak dalam proses pembelajaran di rumah atau daring. Adapun upaya yang dilakukan untuk peningkatan pengetahuan dan kemampuan anak yang tidak hanya berfokus pada kognitif saja namun juga perkembangan karakter, spiritual, fisik, sosial dan emosional maka perlu adanya wadah berbagi ilmu dan praktik berbasis ICT, yaitu KMS (Knowledge Management System). Penelitian ini bertujuan untuk membangun KMS PAUD. Metode yang digunakan adalah KMSLC yang tahapannya terdiri atas evaluasi infrastruktur, pembentukan tim kms, knowledge capture, Design KMS Blueprint, implementasi KMS, pengujian KMS. Penelitian ini menghasilkan aplikasi KMS yang terdiri dari fitur permainan edukasi anak 1-6 tahun, fitur pembelajaran untuk anak usia 3-6 tahun, fitur informasi perkembangan anak berdasarkan usia, register, login dan forum orangtua. Aplikasi ini telah diuji menggunakan pengujian balck box dimana hasil dari pengujian 100% fitur yang terdapat pada aplikasi KMS berfungsi dan berjalan sesuai dengan harapan.